Publications - Data Science Chair

Personalization through User Attributes for Transformer-based Sequential Recommendation. Fischer, Elisabeth; Dallmann, Alexander; Hotho, Andreas. 2022.

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@article{fischer2022personalization,
  author = {Fischer, Elisabeth and Dallmann, Alexander and Hotho, Andreas},
  keywords = {selected},
  title = {Personalization through User Attributes for Transformer-based Sequential Recommendation},
  year = 2022
}

Towards Responsible Medical Diagnostics Recommendation Systems. Schlör, Daniel; Hotho, Andreas. 2022.

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The early development and deployment of hospital and healthcare information systems have encouraged the ongoing digitization of processes in hospitals. Many of these processes, which previously required paperwork and telephone arrangements, are now integrated into IT solutions and require physicians and medical staff to interact with appropriate interfaces and tools. Although this shift to digital data management and process support has benefited patient care in many ways, it requires physicians to accurately capture all relevant information digitally for billing and documentation purposes, which takes a lot of time away from actual patient care work. However, systematic collection of healthcare data over a long period of time offers opportunities to improve this process and support medical staff by introducing recommender systems. Based on a practical working example, in this position paper, we will outline the design of a responsible recommender system in the medical context from a technical, application driven perspective and discuss potential design choices and criteria with a specific focus on accountability, safety, and fairness.

@misc{schlor2022towards,
  abstract = {The early development and deployment of hospital and healthcare information systems have encouraged the ongoing digitization of processes in hospitals. Many of these processes, which previously required paperwork and telephone arrangements, are now integrated into IT solutions and require physicians and medical staff to interact with appropriate interfaces and tools. Although this shift to digital data management and process support has benefited patient care in many ways, it requires physicians to accurately capture all relevant information digitally for billing and documentation purposes, which takes a lot of time away from actual patient care work. However, systematic collection of healthcare data over a long period of time offers opportunities to improve this process and support medical staff by introducing recommender systems. Based on a practical working example, in this position paper, we will outline the design of a responsible recommender system in the medical context from a technical, application driven perspective and discuss potential design choices and criteria with a specific focus on accountability, safety, and fairness.},
  author = {Schlör, Daniel and Hotho, Andreas},
  keywords = {selected},
  note = {cite arxiv:2209.03760Comment: 4th FAccTRec Workshop on Responsible Recommendation},
  title = {Towards Responsible Medical Diagnostics Recommendation Systems},
  year = 2022
}

Sequential Item Recommendation in the MOBA Game Dota 2. Dallmann, Alexander; Kohlmann, Johannes; Zoller, Daniel; Hotho, Andreas. In CoRR, abs/2201.08724. 2022.

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@article{DBLP:journals/corr/abs-2201-08724,
  author = {Dallmann, Alexander and Kohlmann, Johannes and Zoller, Daniel and Hotho, Andreas},
  journal = {CoRR},
  keywords = {selected},
  title = {Sequential Item Recommendation in the {MOBA} Game Dota 2},
  volume = {abs/2201.08724},
  year = 2022
}

CoBERT: Scientific Collaboration Prediction via Sequential Recommendation. Koopmann, Tobias; Kobs, Konstantin; Herud, Konstantin; Hotho, Andreas. In 2021 International Conference on Data Mining Workshops (ICDMW), pp. 45–54. 2021.

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Collaborations are an Important factor for scientific success, as the joint work leads to results individual scientists cannot easily reach. Recommending collaborations automatically can alleviate the time consuming and tedious search for potential collaborators. Usually, such recommendation systems rely on graph structures modeling co-authorship of papers and content-based relations such as similar paper keywords. Models are then trained to estimate the probability of links between certain authors in these graphs.In this paper, we argue that the order of papers is crucial for reliably predicting future collaborations, which is not considered by graph-based recommendation systems. We thus propose to reformulate the task of collaboration recommendation as a sequential recommendation task. Here, we aim to predict the next co-author in a chronologically sorted sequence of an author’s collaborators. We introduce CoBERT, a BERT4Rec inspired model, that predicts the sequence’s next co-author and thus a potential collaborator. Since the order of co-authors of a single paper is not that important compared to the overall paper order, we leverage positional embeddings encoding paper positions instead of co-author positions in the sequence. Additionally, we inject content features about every paper and their co-authors. We evaluate CoBERT on two datasets consisting of papers from the field of Artificial Intelligence and the journal PlosOne. We show that CoBERT can outperform graph-based methods and BERT4Rec when predicting the co-authors of the next paper. We make our code and data available.

@inproceedings{koopmann2021cobert,
  abstract = {Collaborations are an Important factor for scientific success, as the joint work leads to results individual scientists cannot easily reach. Recommending collaborations automatically can alleviate the time consuming and tedious search for potential collaborators. Usually, such recommendation systems rely on graph structures modeling co-authorship of papers and content-based relations such as similar paper keywords. Models are then trained to estimate the probability of links between certain authors in these graphs.In this paper, we argue that the order of papers is crucial for reliably predicting future collaborations, which is not considered by graph-based recommendation systems. We thus propose to reformulate the task of collaboration recommendation as a sequential recommendation task. Here, we aim to predict the next co-author in a chronologically sorted sequence of an author’s collaborators. We introduce CoBERT, a BERT4Rec inspired model, that predicts the sequence’s next co-author and thus a potential collaborator. Since the order of co-authors of a single paper is not that important compared to the overall paper order, we leverage positional embeddings encoding paper positions instead of co-author positions in the sequence. Additionally, we inject content features about every paper and their co-authors. We evaluate CoBERT on two datasets consisting of papers from the field of Artificial Intelligence and the journal PlosOne. We show that CoBERT can outperform graph-based methods and BERT4Rec when predicting the co-authors of the next paper. We make our code and data available.},
  author = {Koopmann, Tobias and Kobs, Konstantin and Herud, Konstantin and Hotho, Andreas},
  booktitle = {2021 International Conference on Data Mining Workshops (ICDMW)},
  keywords = {selected},
  month = 12,
  pages = {45-54},
  title = {CoBERT: Scientific Collaboration Prediction via Sequential Recommendation},
  year = 2021
}

Sequential Item Recommendation in the MOBA Game Dota 2. Dallmann, Alexander; Kohlmann, Johannes; Zoller, Daniel; Hotho, Andreas. In 2021 International Conference on Data Mining Workshops (ICDMW), pp. 10–17. 2021.

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Multiplayer Online Battle Arena (MOBA) games such as Dota 2 attract hundreds of thousands of players every year. Despite the large player base, it is still important to attract new players to prevent the community of a game from becoming inactive. Entering MOBA games is, however, often demanding, requiring the player to learn numerous skills at once. An important factor of success is buying the correct items which forms a complex task depending on various in-game factors such as already purchased items, the team composition, or available resources. A recommendation system can support players by reducing the mental effort required to choose a suitable item, helping, e.g., newer players or players returning to the game after a longer break, to focus on other aspects of the game. Since Sequential Item Recommendation (SIR) has proven to be effective in various domains (e.g. e-commerce, movie recommendation or playlist continuation), we explore the applicability of well-known SIR models in the context of purchase recommendations in Dota 2. To facilitate this research, we collect, analyze and publish Dota-350k, a new large dataset based on recent Dota 2 matches. We find that SIR models can be employed effectively for item recommendation in Dota 2. Our results show that models that consider the order of purchases are the most effective. In contrast to other domains, we find RNN-based models to outperform the more recent Transformer-based architectures on Dota-350k.

@inproceedings{9679858,
  abstract = {Multiplayer Online Battle Arena (MOBA) games such as Dota 2 attract hundreds of thousands of players every year. Despite the large player base, it is still important to attract new players to prevent the community of a game from becoming inactive. Entering MOBA games is, however, often demanding, requiring the player to learn numerous skills at once. An important factor of success is buying the correct items which forms a complex task depending on various in-game factors such as already purchased items, the team composition, or available resources. A recommendation system can support players by reducing the mental effort required to choose a suitable item, helping, e.g., newer players or players returning to the game after a longer break, to focus on other aspects of the game. Since Sequential Item Recommendation (SIR) has proven to be effective in various domains (e.g. e-commerce, movie recommendation or playlist continuation), we explore the applicability of well-known SIR models in the context of purchase recommendations in Dota 2. To facilitate this research, we collect, analyze and publish Dota-350k, a new large dataset based on recent Dota 2 matches. We find that SIR models can be employed effectively for item recommendation in Dota 2. Our results show that models that consider the order of purchases are the most effective. In contrast to other domains, we find RNN-based models to outperform the more recent Transformer-based architectures on Dota-350k.},
  author = {Dallmann, Alexander and Kohlmann, Johannes and Zoller, Daniel and Hotho, Andreas},
  booktitle = {2021 International Conference on Data Mining Workshops (ICDMW)},
  keywords = {from:hotho},
  month = 12,
  pages = {10-17},
  title = {Sequential Item Recommendation in the MOBA Game Dota 2},
  year = 2021
}

A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models. Dallmann, Alexander; Zoller, Daniel; Hotho, Andreas. In Fifteenth ACM Conference on Recommender Systems. ACM, 2021.

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@inproceedings{dallmann2021study,
  author = {Dallmann, Alexander and Zoller, Daniel and Hotho, Andreas},
  booktitle = {Fifteenth {ACM} Conference on Recommender Systems},
  keywords = {selected},
  month = {09},
  publisher = {{ACM}},
  title = {A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models},
  year = 2021
}

Leveraging User-Interactions for Time-Aware Tag Recommendations. Zoller, Daniel; Doerfel, Stephan; Pölitz, Christian; Hotho, Andreas. In Proceedings of the Workshop on Temporal Reasoning in Recommender Systems, of CEUR Workshop Proceedings. 2017.

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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 = {selected},
  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, abs/1706.10231. 2017.

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@article{journals/corr/DallmannGPZH17,
  author = {Dallmann, Alexander and Grimm, Alexander and Pölitz, Christian and Zoller, Daniel and Hotho, Andreas},
  journal = {CoRR},
  keywords = {selected},
  title = {Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time.},
  volume = {abs/1706.10231},
  year = 2017
}

The sixth ACM RecSys workshop on recommender systems and the social web. Jannach, Dietmar; Freyne, Jill; Geyer, Werner; Guy, Ido; Hotho, Andreas; Mobasher, Bamshad. In Eighth ACM Conference on Recommender Systems, RecSys ’14, Foster City, Silicon Valley, CA, USA - October 06 - 10, 2014, p. 395. 2014.

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@inproceedings{jannach2014sixth,
  author = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad},
  booktitle = {Eighth {ACM} Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, {USA} - October 06 - 10, 2014},
  keywords = {from:hotho},
  pages = 395,
  title = {The sixth {ACM} RecSys workshop on recommender systems and the social web},
  year = 2014
}

Tag Recommendations for SensorFolkSonomies. Mueller, Juergen; Doerfel, Stephan; Becker, Martin; Hotho, Andreas; Stumme, Gerd. In Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings, Vol. 1066. CEUR-WS, Aachen, Germany, 2013.

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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 = {from:hotho},
  publisher = {CEUR-WS},
  title = {Tag Recommendations for SensorFolkSonomies},
  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, 1310.1498. 2013.

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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},
  title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations},
  volume = {1310.1498},
  year = 2013
}

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 of SpringerBriefs in Electrical and Computer Engineering. Springer, 2012.

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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 = {bookmarking},
  month = {02},
  publisher = {Springer},
  series = {SpringerBriefs in Electrical and Computer Engineering},
  title = {Recommender Systems for Social Tagging Systems},
  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, Vol. 32, J. J. Pazos Arias, A. Fernández Vilas, R. P. Díaz Redondo (eds.), pp. 65–87. Springer, Berlin/Heidelberg, 2012.

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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 = {bookmarking},
  pages = {65–87},
  publisher = {Springer},
  series = {Intelligent Systems Reference Library},
  title = {Challenges in Tag Recommendations for Collaborative Tagging Systems},
  volume = 32,
  year = 2012
}

Leveraging publication metadata and social data into FolkRank for scientific publication recommendation. Doerfel, Stephan; Jäschke, Robert; Hotho, Andreas; Stumme, Gerd. In Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web, of RSWeb ’12, pp. 9–16. ACM, Dublin, Ireland, 2012.

[ Abstract ]
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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},
  pages = {9–16},
  publisher = {ACM},
  series = {RSWeb '12},
  title = {Leveraging publication metadata and social data into FolkRank for scientific publication recommendation},
  year = 2012
}

The challenge of recommender systems challenges. Said, Alan; Tikk, Domonkos; Hotho, Andreas. In RecSys, P. Cunningham, N. J. Hurley, I. Guy, S. S. Anand (eds.), pp. 9–10. ACM, 2012.

[ 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 = {from:hotho},
  pages = {9-10},
  publisher = {ACM},
  title = {The challenge of recommender systems challenges.},
  year = 2012
}

Recommendation in the Social Web. Burke, Robin; Gemmell, Jonathan; Hotho, Andreas; Jäschke, Robert. In AI Magazine, 32(3), pp. 46–56. Association for the Advancement of Artificial Intelligence, 2011.

[ 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 = {from:hotho},
  number = 3,
  pages = {46–56},
  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 Knowledge Processing and Data Analysis, Vol. 6581 of Lecture Notes in Computer Science, K. E. Wolff, D. E. Palchunov, N. G. Zagoruiko, U. Andelfinger (eds.), pp. 136–149. Springer, Berlin/Heidelberg, 2011.

[ 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 = {content},
  pages = {136–149},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems},
  volume = 6581,
  year = 2011
}

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 (eds.), pp. 615–644. Springer, 2011.

[ 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 = {from:hotho},
  pages = {615-644},
  publisher = {Springer},
  title = {Social Tagging Recommender Systems.},
  year = 2011
}

Testing and Evaluating Tag Recommenders in a Live System. Jäschke, Robert; Eisterlehner, Folke; Hotho, Andreas; Stumme, Gerd. In RecSys ’09: Proceedings of the third ACM Conference on Recommender Systems, D. Benz, F. Janssen (eds.), pp. 369–372. 2009.

[ 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},
  pages = {369–372},
  title = {Testing and Evaluating Tag Recommenders in a Live System},
  year = 2009
}

Tag Recommendations in Social Bookmarking Systems. Jäschke, Robert; Marinho, Leandro; Hotho, Andreas; Schmidt-Thieme, Lars; Stumme, Gerd. In AI Communications, 21(4), E. Giunchiglia (ed.), pp. 231–247. IOS Press, Amsterdam, 2008.

[ 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 = {bookmarking},
  number = 4,
  pages = {231-247},
  publisher = {IOS Press},
  title = {Tag Recommendations in Social Bookmarking Systems},
  volume = 21,
  year = 2008
}

Tag Recommendations in Folksonomies. Jäschke, Robert; Marinho, Leandro; Hotho, Andreas; Schmidt-Thieme, Lars; Stumme, Gerd. In Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007), A. Hinneburg (ed.), pp. 13–20. Martin-Luther-Universität Halle-Wittenberg, 2007.

[ 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 = {bookmarking},
  month = {09},
  pages = {13-20},
  publisher = {Martin-Luther-Universität Halle-Wittenberg},
  title = {Tag Recommendations in Folksonomies},
  year = 2007
}

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 (eds.), pp. 143–158. Springer, 2006.

[ 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 = {ontology},
  pages = {143–158},
  publisher = {Springer},
  title = {Personalized Information Access in a Bibliographic Peer-to-Peer System},
  year = 2006
}

Collaborative and Usage-Driven Evolution of Personal Ontologies. Haase, Peter; Hotho, Andreas; Schmidt-Thieme, Lars; Sure, York. In ESWC, Vol. 3532 of Lecture Notes in Computer Science, A. G\’omez-P\’erez, J. Euzenat (eds.), pp. 486–499. Springer, 2005.

[ BibTeX ]

@inproceedings{DBLP:conf/esws/HaaseHSS05,
  author = {Haase, Peter and Hotho, Andreas and Schmidt-Thieme, Lars and Sure, York},
  booktitle = {ESWC},
  editor = {G{\'o}mez-P{\'e}rez, Asunci{\'o}n and Euzenat, J{\'e}r{\^o}me},
  keywords = {ontology},
  pages = {486-499},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Collaborative and Usage-Driven Evolution of Personal Ontologies.},
  volume = 3532,
  year = 2005
}

Personalized Information Access in a Bibliographic Peer-to-Peer System. Haase, Peter; Ehrig, Marc; Hotho, Andreas; Schnizler, Bj\"orn. In Proceedings of the AAAI Workshop on Semantic Web Personalization, 2004, pp. 1–12. AAAI Press, 2004.

[ BibTeX ]
[ URL ]

@inproceedings{haase04personalized,
  author = {Haase, Peter and Ehrig, Marc and Hotho, Andreas and Schnizler, Bj{\"{o}}rn},
  booktitle = {Proceedings of the AAAI Workshop on Semantic Web Personalization, 2004},
  keywords = {from:hotho},
  month = {07},
  pages = {1-12},
  publisher = {AAAI Press},
  title = {Personalized Information Access in a Bibliographic Peer-to-Peer System},
  year = 2004
}

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