Alexander Dallmann, Dipl. Inf.

2021

1.
Dallmann, A., Zoller, D., Hotho, A.: A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models. Fifteenth ACM Conference on Recommender Systems. ACM (2021).
[ BibTeX ] [ DOI ] [ Download ]
 
@inproceedings{dallmann2021study, author = {Dallmann, Alexander and Zoller, Daniel and Hotho, Andreas}, booktitle = {Fifteenth {ACM} Conference on Recommender Systems}, keywords = {myown sys:relevantfor:dmir recommender_systems evaluation deep_learning information_retrieval research:recommender research:recommender:selected}, month = {sep}, publisher = {{ACM}}, title = {A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models}, year = 2021 }

2020

1.
Fischer, E., Zoller, D., Dallmann, A., Hotho, A.: Integrating Keywords into BERT4Rec for Sequential Recommendation. KI 2020: Advances in Artificial Intelligence (2020).
[ Abstract ] [ BibTeX ] [ DOI ] [ Download ]
 
A crucial part of recommender systems is to model the user’s preference based on her previous interactions. Different neural networks (e.g., Recurrent Neural Networks), that predict the next item solely based on the sequence of interactions have been successfully applied to sequential recommendation. Recently, BERT4Rec has been proposed, which adapts the BERT architecture based on the Transformer model and training methods used in the Neural Language Modeling community to this task. However, BERT4Rec still only relies on item identifiers to model the user preference, ignoring other sources of information. Therefore, as a first step to include additional information, we propose KeBERT4Rec, a modification of BERT4Rec, which utilizes keyword descriptions of items. We compare two variants for adding keywords to the model on two datasets, a Movielens dataset and a dataset of an online fashion store. First results show that both versions of our model improves the sequential recommending task compared to BERT4Rec.
@inproceedings{fischer2020integrating, abstract = {A crucial part of recommender systems is to model the user’s preference based on her previous interactions. Different neural networks (e.g., Recurrent Neural Networks), that predict the next item solely based on the sequence of interactions have been successfully applied to sequential recommendation. Recently, BERT4Rec has been proposed, which adapts the BERT architecture based on the Transformer model and training methods used in the Neural Language Modeling community to this task. However, BERT4Rec still only relies on item identifiers to model the user preference, ignoring other sources of information. Therefore, as a first step to include additional information, we propose KeBERT4Rec, a modification of BERT4Rec, which utilizes keyword descriptions of items. We compare two variants for adding keywords to the model on two datasets, a Movielens dataset and a dataset of an online fashion store. First results show that both versions of our model improves the sequential recommending task compared to BERT4Rec.}, author = {Fischer, Elisabeth and Zoller, Daniel and Dallmann, Alexander and Hotho, Andreas}, booktitle = {KI 2020: Advances in Artificial Intelligence}, keywords = {myown kebert4rec sequential recommendation embedding bert4rec selected}, title = {Integrating Keywords into BERT4Rec for Sequential Recommendation}, year = 2020 }

2018

1.
Hettinger, L., Dallmann, A., Zehe, A., Niebler, T., Hotho, A.: ClaiRE at SemEval-2018 Task 7 - Extended Version, http://arxiv.org/abs/1804.05825, (2018).
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In this paper we describe our post-evaluation results for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). Due to space limitations we publish an extended version of Hettinger et al. (2018) including further technical details and changes made to the preprocessing step in the post-evaluation phase. Due to these changes Classification of Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75.11% for the first subtask and 81.44% for the second.
@misc{hettinger2018claire, abstract = {In this paper we describe our post-evaluation results for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). Due to space limitations we publish an extended version of Hettinger et al. (2018) including further technical details and changes made to the preprocessing step in the post-evaluation phase. Due to these changes Classification of Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75.11% for the first subtask and 81.44% for the second.}, author = {Hettinger, Lena and Dallmann, Alexander and Zehe, Albin and Niebler, Thomas and Hotho, Andreas}, keywords = {myown semeval sys:relevantfor:dmir challenge relation_exctraction deep_learning}, note = {cite arxiv:1804.05825Comment: This is the extended version for our work: ClaiRE at SemEval-2018 Task 7}, title = {ClaiRE at SemEval-2018 Task 7 - Extended Version}, year = 2018 }

2016

1.
Dallmann, A., Niebler, T., Lemmerich, F., Hotho, A.: Extracting Semantics from Random Walks on Wikipedia: Comparing learning and counting methods. In: West, R., Zia, L., Taraborelli, D., and Leskovec, J. (eds.) Wiki Workshop@ICWSM (2016).
[ Abstract ] [ BibTeX ] [ URL ] [ Download ]
 
Semantic relatedness between words has been extracted from a variety of sources. In this ongoing work, we explore and compare several options for determining if semantic relatedness can be extracted from navigation structures in Wikipedia. In that direction, we first investigate the potential of representation learning techniques such as DeepWalk in comparison to previously applied methods based on counting co-occurrences. Since both methods are based on (random) paths in the network, we also study different approaches to generate paths from Wikipedia link structure. For this task, we do not only consider the link structure of Wikipedia, but also actual navigation behavior of users. Finally, we analyze if semantics can also be extracted from smaller subsets of the Wikipedia link network. As a result we find that representa- tion learning techniques mostly outperform the investigated co-occurrence counting methods on the Wikipedia network. However, we find that this is not the case for paths sampled from human navigation behavior.
@inproceedings{dallmann2016extracting, abstract = {Semantic relatedness between words has been extracted from a variety of sources. In this ongoing work, we explore and compare several options for determining if semantic relatedness can be extracted from navigation structures in Wikipedia. In that direction, we first investigate the potential of representation learning techniques such as DeepWalk in comparison to previously applied methods based on counting co-occurrences. Since both methods are based on (random) paths in the network, we also study different approaches to generate paths from Wikipedia link structure. For this task, we do not only consider the link structure of Wikipedia, but also actual navigation behavior of users. Finally, we analyze if semantics can also be extracted from smaller subsets of the Wikipedia link network. As a result we find that representa- tion learning techniques mostly outperform the investigated co-occurrence counting methods on the Wikipedia network. However, we find that this is not the case for paths sampled from human navigation behavior.}, author = {Dallmann, Alexander and Niebler, Thomas and Lemmerich, Florian and Hotho, Andreas}, booktitle = {Wiki Workshop@ICWSM}, editor = {West, Robert and Zia, Leila and Taraborelli, Dario and Leskovec, Jure}, keywords = {myown semantic sys:relevantfor:dmir random_walk wikipedia}, title = {Extracting Semantics from Random Walks on Wikipedia: Comparing learning and counting methods}, year = 2016 }

2015

1.
Dallmann, A., Lemmerich, F., Zoller, D., Hotho, A.: Media Bias in German Online Newspapers. 26th ACM Conference on Hypertext and Social Media. ACM, Cyprus, Turkey, September 1-4 (2015).
[ BibTeX ]
 
@inproceedings{dallmann2015media, address = {Cyprus, Turkey, September 1-4}, author = {Dallmann, Alexander and Lemmerich, Florian and Zoller, Daniel and Hotho, Andreas}, booktitle = {26th ACM Conference on Hypertext and Social Media}, keywords = {myown sys:relevantfor:dmir bias media}, publisher = {ACM}, title = {Media Bias in German Online Newspapers}, year = 2015 }

2014

1.
Dallmann, A., Beck, P.-D., von Gudenberg, J.: Finding Enclosures for Linear Systems Using Interval Matrix Multiplication in CUDA. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., and Waśniewski, J. (eds.) Parallel Processing and Applied Mathematics. pp. 582–590. Springer Berlin Heidelberg (2014).
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ]
 
In this paper we present CUDA kernels that compute an interval matrix product. Starting from a naive implementation we investigate possible speedups using commonly known techniques from standard matrix multiplication. We also evaluate the achieved speedup when our kernels are used to accelerate a variant of an existing algorithm that finds an enclosure for the solution of a linear system. Moreover the quality of our enclosure is discussed.
@incollection{noKey, abstract = {In this paper we present CUDA kernels that compute an interval matrix product. Starting from a naive implementation we investigate possible speedups using commonly known techniques from standard matrix multiplication. We also evaluate the achieved speedup when our kernels are used to accelerate a variant of an existing algorithm that finds an enclosure for the solution of a linear system. Moreover the quality of our enclosure is discussed.}, author = {Dallmann, Alexander and Beck, Philip-Daniel and von Gudenberg, JürgenWolff}, booktitle = {Parallel Processing and Applied Mathematics}, editor = {Wyrzykowski, Roman and Dongarra, Jack and Karczewski, Konrad and Waśniewski, Jerzy}, keywords = {cuda myown linear_systems enclosures}, pages = {582-590}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, title = {Finding Enclosures for Linear Systems Using Interval Matrix Multiplication in CUDA}, volume = 8385, year = 2014 }