Elisabeth Fischer, M.Sc.

Chair of Data Science (Informatik X)
University of Würzburg
Am Hubland
97074 Würzburg
Germany

Email: elisabeth.fischer[at]informatik.uni-wuerzburg.de

Phone: (+49 931) 31-89539

Office: Room 50.03.018 (Building G50, Institutsgebäude Künstliche Intelligenz)

Projects and Research Interests

I joined the DMIR group for my PhD studies after receiving my masters degree in Computer Science at the university of Würzburg in spring 2017.

I'm now working on the topic of personalization and recommendation for web shops as part of the research agreement with Adidas.

Teaching

Seminar "Selected Topics of Machine Learning":

Publications

[ 2022 ]
[ 2021 ]
[ 2020 ]

2022[ to top ]

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 = {session}, title = {Personalization through User Attributes for Transformer-based Sequential Recommendation}, year = 2022 }

2021[ to top ]

Comparison of Transformer-Based Sequential ProductRecommendation Models for the Coveo Data Challenge Fischer, Elisabeth; Zoller, Daniel; Hotho, Andreas in SIGIR Workshop On eCommerce (2021).
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@article{fischer2021comparison, author = {Fischer, Elisabeth and Zoller, Daniel and Hotho, Andreas}, journal = {SIGIR Workshop On eCommerce}, keywords = {recommendation}, month = {July}, title = {Comparison of Transformer-Based Sequential ProductRecommendation Models for the Coveo Data Challenge}, year = 2021 }

2020[ to top ]

Integrating Keywords into BERT4Rec for Sequential Recommendation Fischer, Elisabeth; Zoller, Daniel; Dallmann, Alexander; Hotho, Andreas in KI 2020: Advances in Artificial Intelligence (2020).
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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 = {recommendation}, title = {Integrating Keywords into BERT4Rec for Sequential Recommendation}, year = 2020 }

Projects and Research Interests

Teaching

Publications

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