Recommender Systems - Publications

2021 [ to top ]

Comparison of Transformer-Based Sequential Product Recommendation Models for the Coveo Data Challenge. Fischer, Elisabeth; Zoller, Daniel; Hotho, Andreas in SIGIR Workshop On eCommerce (2021).
[ BibTeX ] [ Download ]
 
@article{fischer2021comparison, author = {Fischer, Elisabeth and Zoller, Daniel and Hotho, Andreas}, journal = {SIGIR Workshop On eCommerce}, keywords = {from:e.fischer myown project:adidas recommendation research:recommender research:recommender:selected transformer}, month = {July}, title = {Comparison of Transformer-Based Sequential Product Recommendation Models for the Coveo Data Challenge}, year = 2021 }

A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models. Dallmann, Alexander; Zoller, Daniel; Hotho, Andreas (2021).
[ BibTeX ] [ DOI ] [ Download ]
 
@inproceedings{dallmann2021study, author = {Dallmann, Alexander and Zoller, Daniel and Hotho, Andreas}, booktitle = {Fifteenth {ACM} Conference on Recommender Systems}, keywords = {deep_learning evaluation information_retrieval myown recommender_systems research:recommender research:recommender:selected from:dallmann}, month = {sep}, publisher = {{ACM}}, title = {A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models}, year = 2021 }

2020 [ to top ]

Integrating Keywords into BERT4Rec for Sequential Recommendation. Fischer, Elisabeth; Zoller, Daniel; Dallmann, Alexander; Hotho, Andreas (2020).
[ BibTeX ] [ Download ]
 
@inproceedings{fischer2020integrating, author = {Fischer, Elisabeth and Zoller, Daniel and Dallmann, Alexander and Hotho, Andreas}, booktitle = {KI 2020: Advances in Artificial Intelligence}, keywords = {bert4rec embedding from:nosebrain kebert4rec keyword myown project:adidas recommendation research:recommender research:recommender:selected selected sequential}, title = {Integrating Keywords into BERT4Rec for Sequential Recommendation}, year = 2020 }

2017 [ to top ]

Leveraging User-Interactions for Time-Aware Tag Recommendations. Zoller, Daniel; Doerfel, Stephan; Pölitz, Christian; Hotho, Andreas in CEUR Workshop Proceedings (2017).
[ Abstract ] [ BibTeX ] [ Download ]
 
For the popular task of tag recommendation, various (complex) approaches have been proposed. Recently however, research has focused on heuristics with low computational effort and particularly, a time-aware heuristic, called BLL, has been shown to compare well to various state-of-the-art methods. Here, we follow up on these results by presenting another time-aware approach leveraging user interaction data in an easily interpretable, on-the-fly computable approach that can successfully be combined with BLL. We investigate the influence of time as a parameter in that approach, and we demonstrate the effectiveness of the proposed method using two datasets from the popular public social tagging system BibSonomy.
@inproceedings{zoller2017leveraging, abstract = {For the popular task of tag recommendation, various (complex) approaches have been proposed. Recently however, research has focused on heuristics with low computational effort and particularly, a time-aware heuristic, called BLL, has been shown to compare well to various state-of-the-art methods. Here, we follow up on these results by presenting another time-aware approach leveraging user interaction data in an easily interpretable, on-the-fly computable approach that can successfully be combined with BLL. We investigate the influence of time as a parameter in that approach, and we demonstrate the effectiveness of the proposed method using two datasets from the popular public social tagging system BibSonomy.}, author = {Zoller, Daniel and Doerfel, Stephan and Pölitz, Christian and Hotho, Andreas}, booktitle = {Proceedings of the Workshop on Temporal Reasoning in Recommender Systems}, keywords = {bibsonomy:selected from:nosebrain interaction myown project:bibsonomy recommendation recommender research:recommender research:recommender:selected tag user}, series = {{CEUR} Workshop Proceedings}, title = {Leveraging User-Interactions for Time-Aware Tag Recommendations}, year = 2017 }

Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time. Dallmann, Alexander; Grimm, Alexander; Pölitz, Christian; Zoller, Daniel; Hotho, Andreas in CoRR (2017). abs/1706.10231
[ BibTeX ] [ URL ] [ Download ]
 
@article{journals/corr/DallmannGPZH17, author = {Dallmann, Alexander and Grimm, Alexander and Pölitz, Christian and Zoller, Daniel and Hotho, Andreas}, journal = {CoRR}, keywords = {myown recommender research:recommender research:recommender:selected session}, title = {Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time.}, volume = {abs/1706.10231}, year = 2017 }

2014 [ to top ]

Proceedings of the 6th Workshop on Recommender Systems and the Social Web (RSWeb 2014) co-located with the 8th ACM Conference on Recommender Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014 Jannach, Dietmar; Freyne, Jill; Geyer, Werner; Guy, Ido; Hotho, Andreas; Mobasher, Bamshad in CEUR Workshop Proceedings (2014). (Bd. 1271) CEUR-WS.org.
[ BibTeX ] [ URL ]
 
@proceedings{jannach2014proceedings, editor = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, keywords = {2014 from:hotho myown proceedings recommender research:recommender social workshop}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Proceedings of the 6th Workshop on Recommender Systems and the Social Web (RSWeb 2014) co-located with the 8th {ACM} Conference on Recommender Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014}, volume = 1271, year = 2014 }

2013 [ to top ]

Tag Recommendations for SensorFolkSonomies. Mueller, Juergen; Doerfel, Stephan; Becker, Martin; Hotho, Andreas; Stumme, Gerd (2013). (Bd. 1066)
[ Abstract ] [ BibTeX ] [ URL ] [ Download ]
 
With the rising popularity of smart mobile devices, sensor data-based applications have become more and more popular. Their users record data during their daily routine or specifically for certain events. The application WideNoise Plus allows users to record sound samples and to annotate them with perceptions and tags. The app is being used to document and map the soundscape all over the world. The procedure of recording, including the assignment of tags, has to be as easy-to-use as possible. We therefore discuss the application of tag recommender algorithms in this particular scenario. We show, that this task is fundamentally different from the well-known tag recommendation problem in folksonomies as users do no longer tag fix resources but rather sensory data and impressions. The scenario requires efficient recommender algorithms that are able to run on the mobile device, since Internet connectivity cannot be assumed to be available. Therefore, we evaluate the performance of several tag recommendation algorithms and discuss their applicability in the mobile sensing use-case.
@inproceedings{mueller2013recommendations, abstract = {With the rising popularity of smart mobile devices, sensor data-based applications have become more and more popular. Their users record data during their daily routine or specifically for certain events. The application WideNoise Plus allows users to record sound samples and to annotate them with perceptions and tags. The app is being used to document and map the soundscape all over the world. The procedure of recording, including the assignment of tags, has to be as easy-to-use as possible. We therefore discuss the application of tag recommender algorithms in this particular scenario. We show, that this task is fundamentally different from the well-known tag recommendation problem in folksonomies as users do no longer tag fix resources but rather sensory data and impressions. The scenario requires efficient recommender algorithms that are able to run on the mobile device, since Internet connectivity cannot be assumed to be available. Therefore, we evaluate the performance of several tag recommendation algorithms and discuss their applicability in the mobile sensing use-case.}, address = {Aachen, Germany}, author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd}, booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China – October 12-16, 2013. Proceedings}, keywords = {2013 everyaware folksonomy from:hotho myown recommender research:recommender sensor tag}, publisher = {CEUR-WS}, title = {Tag Recommendations for SensorFolkSonomies}, volume = 1066, year = 2013 }

Proceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China, October 13, 2013. Mobasher, Bamshad; Jannach, Dietmar; Geyer, Werner; Freyne, Jill; Hotho, Andreas; Anand, Sarabjot Singh; Guy, Ido in CEUR Workshop Proceedings (2013). (Bd. 1066) CEUR-WS.org.
[ BibTeX ] [ URL ]
 
@proceedings{conf/recsys/2013rsweb, booktitle = {RSWeb@RecSys}, editor = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Freyne, Jill and Hotho, Andreas and Anand, Sarabjot Singh and Guy, Ido}, keywords = {2013 bibsonomy from:hotho l3s myown recommender research:recommender social web workshop}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Proceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China, October 13, 2013.}, volume = 1066, year = 2013 }

Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations. Landia, Nikolas; Doerfel, Stephan; Jäschke, Robert; Anand, Sarabjot Singh; Hotho, Andreas; Griffiths, Nathan in cs.IR (2013). 1310.1498
[ Abstract ] [ BibTeX ] [ URL ]
 
The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.
@article{landia2013deeper, abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.}, author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan}, journal = {cs.IR}, keywords = {bookmarking collaborative folkrank folksonomy from:hotho graph myown recommender research:recommender social tagging}, title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations}, volume = {1310.1498}, year = 2013 }

2012 [ to top ]

The challenge of recommender systems challenges. Said, Alan; Tikk, Domonkos; Hotho, Andreas P. Cunningham, N. J. Hurley, I. Guy, S. S. Anand (Hrsg.) (2012). 9–10.
[ BibTeX ] [ URL ]
 
@inproceedings{conf/recsys/SaidTH12, author = {Said, Alan and Tikk, Domonkos and Hotho, Andreas}, booktitle = {RecSys}, crossref = {conf/recsys/2012}, editor = {Cunningham, Padraig and Hurley, Neil J. and Guy, Ido and Anand, Sarabjot Singh}, keywords = {2012 from:hotho myown recommender research:recommender}, pages = {9-10}, publisher = {ACM}, title = {The challenge of recommender systems challenges.}, year = 2012 }

RSWeb ’12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web Mobasher, Bamshad; Jannach, Dietmar; Geyer, Werner; Hotho, Andreas (2012). ACM, Dublin, Ireland.
[ Abstract ] [ BibTeX ]
 
The new opportunities for applying recommendation techniques within Social Web platforms and applications as well as the various new sources of information which have become available in the Web 2.0 and can be incorporated in future recommender applications are a strong driving factor in current recommender system research for various reasons:</p> <p>(1) Social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation.</p> <p>(2) New application areas for recommender systems emerge with the popularity of the Social Web. Recommenders cannot only be used to sort and filter Web 2.0 and social network information, they can also support users in the information sharing process, e.g., by recommending suitable tags during folksonomy development.</p> <p>(3) Recommender technology can assist Social Web systems through increasing adoption and participation and sustaining membership. Through targeted and timely intervention which stimulates traffic and interaction, recommender technology can play its role in sustaining the success of the Social Web.</p> <p>(4) The Social Web also presents new challenges for recommender systems, such as the complicated nature of human-to-human interaction which comes into play when recommending people and can require more interactive and richer recommender systems user interfaces.</p> <p>The technical papers appearing in these proceedings aim to explore and understand challenges and new opportunities for recommender systems in the Social Web and were selected in a formal review process by an international program committee.</p> <p>Overall, we received 13 paper submissions from 12 different countries, out of which 7 long papers and 1 short paper were selected for presentation and inclusion in the proceedings. The submitted papers addressed a variety of topics related to Social Web recommender systems from the use of microblogging data for personalization over new tag recommendation approaches to social media-based personalization of news.
@proceedings{Mobasher:2012:2365934, abstract = {The new opportunities for applying recommendation techniques within Social Web platforms and applications as well as the various new sources of information which have become available in the Web 2.0 and can be incorporated in future recommender applications are a strong driving factor in current recommender system research for various reasons:</p> <p>(1) Social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation.</p> <p>(2) New application areas for recommender systems emerge with the popularity of the Social Web. Recommenders cannot only be used to sort and filter Web 2.0 and social network information, they can also support users in the information sharing process, e.g., by recommending suitable tags during folksonomy development.</p> <p>(3) Recommender technology can assist Social Web systems through increasing adoption and participation and sustaining membership. Through targeted and timely intervention which stimulates traffic and interaction, recommender technology can play its role in sustaining the success of the Social Web.</p> <p>(4) The Social Web also presents new challenges for recommender systems, such as the complicated nature of human-to-human interaction which comes into play when recommending people and can require more interactive and richer recommender systems user interfaces.</p> <p>The technical papers appearing in these proceedings aim to explore and understand challenges and new opportunities for recommender systems in the Social Web and were selected in a formal review process by an international program committee.</p> <p>Overall, we received 13 paper submissions from 12 different countries, out of which 7 long papers and 1 short paper were selected for presentation and inclusion in the proceedings. The submitted papers addressed a variety of topics related to Social Web recommender systems from the use of microblogging data for personalization over new tag recommendation approaches to social media-based personalization of news.}, address = {New York, NY, USA}, author = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Hotho, Andreas}, keywords = {2012 acm from:hotho myown recommender research:recommender rsweb social web workshop}, note = 609126, publisher = {ACM}, title = {RSWeb '12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, year = 2012 }

Recommender Systems for Social Tagging Systems Balby Marinho, L.; Hotho, A.; Jäschke, R.; Nanopoulos, A.; Rendle, S.; Schmidt-Thieme, L.; Stumme, G.; Symeonidis, P. in SpringerBriefs in Electrical and Computer Engineering (2012). Springer.
[ Abstract ] [ BibTeX ] [ URL ] [ Download ]
 
Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.
@book{balbymarinho2012recommender, abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.}, author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.}, keywords = {2012 bookmarking collaborative folksonomy from:hotho myown recommender research:recommender social tagging}, month = {feb}, publisher = {Springer}, series = {SpringerBriefs in Electrical and Computer Engineering}, title = {Recommender Systems for Social Tagging Systems}, year = 2012 }

Leveraging publication metadata and social data into FolkRank for scientific publication recommendation. Doerfel, Stephan; Jäschke, Robert; Hotho, Andreas; Stumme, Gerd in RSWeb ’12 (2012). 9–16.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ]
 
The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.
@inproceedings{Doerfel:2012:LPM:2365934.2365937, abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.}, address = {New York, NY, USA}, author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, keywords = {bookmarking folkrank from:hotho myown recommender research:recommender social tagging}, pages = {9&ndash;16}, publisher = {ACM}, series = {RSWeb '12}, title = {Leveraging publication metadata and social data into FolkRank for scientific publication recommendation}, year = 2012 }

Challenges in Tag Recommendations for Collaborative Tagging Systems. Jäschke, Robert; Hotho, Andreas; Mitzlaff, Folke; Stumme, Gerd in Recommender Systems for the Social Web, J. J. Pazos Arias, A. Fernández Vilas, R. P. Díaz Redondo (Hrsg.) (2012). (Bd. 32) 65–87.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.
@incollection{jaeschke2012challenges, abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.}, address = {Berlin/Heidelberg}, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {Recommender Systems for the Social Web}, editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, keywords = {2012 bookmarking challenge collaborative dc09 discovery folksonomy from:hotho myown recommender research:recommender rsdc08 social tagging}, pages = {65&ndash;87}, publisher = {Springer}, series = {Intelligent Systems Reference Library}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, volume = 32, year = 2012 }

4th ACM RecSys workshop on recommender systems and the social web. Mobasher, Bamshad; Jannach, Dietmar; Geyer, Werner; Hotho, Andreas P. Cunningham, N. J. Hurley, I. Guy, S. S. Anand (Hrsg.) (2012). 345–346.
[ BibTeX ] [ URL ]
 
@inproceedings{conf/recsys/MobasherJGH12, author = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Hotho, Andreas}, booktitle = {RecSys}, crossref = {conf/recsys/2012}, editor = {Cunningham, Padraig and Hurley, Neil J. and Guy, Ido and Anand, Sarabjot Singh}, keywords = {2012 from:hotho myown recommender research:recommender workshop}, pages = {345-346}, publisher = {ACM}, title = {4th ACM RecSys workshop on recommender systems and the social web.}, year = 2012 }

2011 [ to top ]

Social Tagging Recommender Systems. Marinho, Leandro Balby; Nanopoulos, Alexandros; Schmidt-Thieme, Lars; Jäschke, Robert; Hotho, Andreas; Stumme, Gerd; Symeonidis, Panagiotis in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, P. B. Kantor (Hrsg.) (2011). 615–644.
[ BibTeX ] [ URL ]
 
@incollection{reference/rsh/MarinhoNSJHSS11, author = {Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis}, booktitle = {Recommender Systems Handbook}, crossref = {reference/rsh/2011}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, keywords = {2011 folksonomy from:hotho myown recommender research:recommender tagging taggingsurvey}, pages = {615-644}, publisher = {Springer}, title = {Social Tagging Recommender Systems.}, year = 2011 }

Recommendation in the Social Web. Burke, Robin; Gemmell, Jonathan; Hotho, Andreas; Jäschke, Robert in AI Magazine (2011). 32(3) 46–56.
[ Abstract ] [ BibTeX ] [ URL ]
 
Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.
@article{burke2011recommendation, abstract = {Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.}, author = {Burke, Robin and Gemmell, Jonathan and Hotho, Andreas and Jäschke, Robert}, journal = {AI Magazine}, keywords = {2011 collaborative from:hotho myown recommender research:recommender social tagging taggingsurvey web}, number = 3, pages = {46&ndash;56}, publisher = {Association for the Advancement of Artificial Intelligence}, title = {Recommendation in the Social Web}, volume = 32, year = 2011 }

A Comparison of Content-Based Tag Recommendations in Folksonomy Systems. Illig, Jens; Hotho, Andreas; Jäschke, Robert; Stumme, Gerd in Lecture Notes in Computer Science, K. E. Wolff, D. E. Palchunov, N. G. Zagoruiko, U. Andelfinger (Hrsg.) (2011). (Bd. 6581) 136–149.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.
@inproceedings{illig2009comparison, abstract = {Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.}, address = {Berlin/Heidelberg}, author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, booktitle = {Knowledge Processing and Data Analysis}, editor = {Wolff, Karl Erich and Palchunov, Dmitry E. and Zagoruiko, Nikolay G. and Andelfinger, Urs}, keywords = {2011 content folksonomy from:hotho myown recommendations recommender research:recommender tag}, pages = {136&ndash;149}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems}, volume = 6581, year = 2011 }

3rd workshop on recommender systems and the social web. Freyne, Jill; Anand, Sarabjot Singh; Guy, Ido; Hotho, Andreas in RecSys ’11 (2011). 383–384.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
The exponential growth of the social web poses challenges and new opportunities for recommender systems. The social web has turned information consumers into active contributors creating massive amounts of information. Finding relevant and interesting content at the right time and in the right context is challenging for existing recommender approaches. At the same time, social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. The Social Web provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.</p> <p>The goal of this one day workshop was to bring together researchers and practitioners to explore, discuss, and understand challenges and new opportunities for Recommender Systems and the Social Web. The workshop consisted both of technical sessions, in which selected participants presented their results or ongoing research, as well as informal breakout sessions on more focused topics.</p> <p>Papers discussing various aspects of recommender system in the Social Web were submitted and selected for presentation and discussion in the workshop in a formal reviewing process: Case studies and novel fielded social recommender applications; Economy of community-based systems: Using recommenders to encourage users to contribute and sustain participation.; Social network and folksonomy development: Recommending friends, tags, bookmarks, blogs, music, communities etc.; Recommender systems mash-ups, Web 2.0 user interfaces, rich media recommender systems; Collaborative knowledge authoring, collective intelligence; Recommender applications involving users or groups directly in the recommendation process; Exploiting folksonomies, social network information, interaction, user context and communities or groups for recommendations; Trust and reputation aware social recommendations; Semantic Web recommender systems, use of ontologies or microformats; Empirical evaluation of social recommender techniques, success and failure measures</p> <p>Full workshop details are available at http://www.dcs.warwick.ac.uk/~ssanand/RSWeb11/index.htm
@inproceedings{Freyne:2011:WRS:2043932.2044014, abstract = {The exponential growth of the social web poses challenges and new opportunities for recommender systems. The social web has turned information consumers into active contributors creating massive amounts of information. Finding relevant and interesting content at the right time and in the right context is challenging for existing recommender approaches. At the same time, social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. The Social Web provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.</p> <p>The goal of this one day workshop was to bring together researchers and practitioners to explore, discuss, and understand challenges and new opportunities for Recommender Systems and the Social Web. The workshop consisted both of technical sessions, in which selected participants presented their results or ongoing research, as well as informal breakout sessions on more focused topics.</p> <p>Papers discussing various aspects of recommender system in the Social Web were submitted and selected for presentation and discussion in the workshop in a formal reviewing process: Case studies and novel fielded social recommender applications; Economy of community-based systems: Using recommenders to encourage users to contribute and sustain participation.; Social network and folksonomy development: Recommending friends, tags, bookmarks, blogs, music, communities etc.; Recommender systems mash-ups, Web 2.0 user interfaces, rich media recommender systems; Collaborative knowledge authoring, collective intelligence; Recommender applications involving users or groups directly in the recommendation process; Exploiting folksonomies, social network information, interaction, user context and communities or groups for recommendations; Trust and reputation aware social recommendations; Semantic Web recommender systems, use of ontologies or microformats; Empirical evaluation of social recommender techniques, success and failure measures</p> <p>Full workshop details are available at http://www.dcs.warwick.ac.uk/&nbsp;ssanand/RSWeb11/index.htm}, address = {New York, NY, USA}, author = {Freyne, Jill and Anand, Sarabjot Singh and Guy, Ido and Hotho, Andreas}, booktitle = {Proceedings of the fifth ACM conference on Recommender systems}, keywords = {2011 cochair from:hotho myown recommender research:recommender social systems workshop}, pages = {383&ndash;384}, publisher = {ACM}, series = {RecSys '11}, title = {3rd workshop on recommender systems and the social web}, year = 2011 }

2009 [ to top ]

Testing and Evaluating Tag Recommenders in a Live System. Jäschke, Robert; Eisterlehner, Folke; Hotho, Andreas; Stumme, Gerd D. Benz, F. Janssen (Hrsg.) (2009). 369–372.
[ Abstract ] [ BibTeX ] [ Download ]
 
The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstrating the power of the framework.
@inproceedings{jaeschke2009testingKDML, abstract = {The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstrating the power of the framework.}, author = {Jäschke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd}, booktitle = {RecSys '09: Proceedings of the third ACM Conference on Recommender Systems}, editor = {Benz, Dominik and Janssen, Frederik}, keywords = {bibsonomy folksonomy framework from:hotho myown project:bibsonomy recommender research:recommender tagging taggingsurvey}, pages = {369&ndash;372}, title = {Testing and Evaluating Tag Recommenders in a Live System}, year = 2009 }

2008 [ to top ]

Tag Recommendations in Social Bookmarking Systems. Jäschke, Robert; Marinho, Leandro; Hotho, Andreas; Schmidt-Thieme, Lars; Stumme, Gerd in AI Communications, (E. Giunchiglia, Hrsg.) (2008). 21(4) 231–247.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ]
 
Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
@article{jaeschke2008tag, abstract = {Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.}, address = {Amsterdam}, author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, editor = {Giunchiglia, Enrico}, journal = {AI Communications}, keywords = {2008 bookmarking myown recommendations recommender research:recommender research:recommender:selected social tagging taggingsurvey}, number = 4, pages = {231-247}, publisher = {IOS Press}, title = {Tag Recommendations in Social Bookmarking Systems}, volume = 21, year = 2008 }

2007 [ to top ]

Tag Recommendations in Folksonomies. Jäschke, Robert; Marinho, Leandro Balby; Hotho, Andreas; Schmidt-Thieme, Lars; Stumme, Gerd in Lecture Notes in Computer Science, J. N. Kok, J. Koronacki, R. L. de Mántaras, S. Matwin, D. Mladenic, A. Skowron (Hrsg.) (2007). (Bd. 4702) 506–514.
[ BibTeX ] [ URL ]
 
@inproceedings{jaschke07recommender, author = {Jäschke, Robert and Marinho, Leandro Balby and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, booktitle = {Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings}, editor = {Kok, Joost N. and Koronacki, Jacek and de Mántaras, Ramon López and Matwin, Stan and Mladenic, Dunja and Skowron, Andrzej}, keywords = {2007 folksonomy from:hotho kdubiq myown pagerank project:bibsonomy recommender research:recommender social sosbuch summerschool tagging taggingsurvey}, pages = {506-514}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Tag Recommendations in Folksonomies}, volume = 4702, year = 2007 }

Tag Recommendations in Folksonomies. Jäschke, Robert; Marinho, Leandro; Hotho, Andreas; Schmidt-Thieme, Lars; Stumme, Gerd A. Hinneburg (Hrsg.) (2007). 13–20.
[ BibTeX ] [ URL ]
 
@inproceedings{jaeschke07tagKdml, author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, booktitle = {Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)}, editor = {Hinneburg, Alexander}, keywords = {2007 bookmarking collaborative filtering folksonomy myown project:bibsonomy recommender research:recommender social}, month = {sep}, pages = {13-20}, publisher = {Martin-Luther-Universität Halle-Wittenberg}, title = {Tag Recommendations in Folksonomies}, year = 2007 }

2006 [ to top ]

Personalized Information Access in a Bibliographic Peer-to-Peer System. Haase, Peter; Ehrig, Marc; Hotho, Andreas; Schnizler, Björn in Peer-to-Peer and SemanticWeb, Decentralized Management and Exchange of Knowledge and Information, S. Staab, H. Stuckenschmidt (Hrsg.) (2006). 143–158.
[ BibTeX ]
 
@incollection{Haase04Personalized, author = {Haase, Peter and Ehrig, Marc and Hotho, Andreas and Schnizler, Björn}, booktitle = {Peer-to-Peer and SemanticWeb, Decentralized Management and Exchange of Knowledge and Information}, editor = {Staab, Steffen and Stuckenschmidt, Heiner}, keywords = {2006 bibliographic from:hotho myown ontology p2p recommender research:recommender}, pages = {143&ndash;158}, publisher = {Springer}, title = {Personalized Information Access in a Bibliographic Peer-to-Peer System}, year = 2006 }

2005 [ to top ]

Collaborative and Usage-Driven Evolution of Personal Ontologies. Haase, Peter; Hotho, Andreas; Schmidt-Thieme, Lars; Sure, York in Lecture Notes in Computer Science, A. Gómez-Pérez, J. Euzenat (Hrsg.) (2005). (Bd. 3532) 486–499.
[ BibTeX ]
 
@inproceedings{DBLP:conf/esws/HaaseHSS05, author = {Haase, Peter and Hotho, Andreas and Schmidt-Thieme, Lars and Sure, York}, booktitle = {ESWC}, editor = {G{ó}mez-P{é}rez, Asunci{ó}n and Euzenat, J{é}r{ô}me}, keywords = {2005 evolution from:hotho myown ontology recommender research:recommender}, pages = {486-499}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Collaborative and Usage-Driven Evolution of Personal Ontologies.}, volume = 3532, year = 2005 }

2004 [ to top ]

Personalized Information Access in a Bibliographic Peer-to-Peer System. Haase, Peter; Ehrig, Marc; Hotho, Andreas; Schnizler, Björn (2004). 1–12.
[ BibTeX ] [ URL ]
 
@inproceedings{haase04personalized, author = {Haase, Peter and Ehrig, Marc and Hotho, Andreas and Schnizler, Bj{{ö}}rn}, booktitle = {Proceedings of the AAAI Workshop on Semantic Web Personalization, 2004}, keywords = {2004 bibliographic from:hotho myown p2p recommender research:recommender}, month = {JUL}, pages = {1-12}, publisher = {AAAI Press}, title = {Personalized Information Access in a Bibliographic Peer-to-Peer System}, year = 2004 }