beeFormer: Bridging the Gap Between Semantic and Interaction Similarity
  in Recommender Systems
Recommender systems often use text-side information to improve their
predictions, especially in cold-start or zero-shot recommendation scenarios,
where traditional collaborative filtering approaches cannot be used. Many
approaches to text-mining side information for recommender systems have been
proposed over recent years, with sentence Transformers being the most prominent
one. However, these models are trained to predict semantic similarity without
utilizing interaction data with hidden patterns specific to recommender
systems. In this paper, we propose beeFormer, a framework for training sentence
Transformer models with interaction data. We demonstrate that our models
trained with beeFormer can transfer knowledge between datasets while
outperforming not only semantic similarity sentence Transformers but also
traditional collaborative filtering methods. We also show that training on
multiple datasets from different domains accumulates knowledge in a single
model, unlocking the possibility of training universal, domain-agnostic
sentence Transformer models to mine text representations for recommender
systems. We release the source code, trained models, and additional details
allowing replication of our experiments at
https://github.com/recombee/beeformer.
66e939a1218585b91b9c81d6
Vojtěch Vančura
claimed_verified
2024-09-17T09:30:33.019Z
6641d8f7f448effbae1a32ca
https://cdn-avatars.huggingface.co/v1/production/uploads/6641d8f7f448effbae1a32ca/mgT3E_Q9rWgHGpeLKHex7.png
Vojtěch Vančura
user
beeformer
66e939a1218585b91b9c81d7
Pavel Kordík
66e939a1218585b91b9c81d8
Milan Straka
admin_assigned
2024-09-17T11:34:56.923Z
607c806188160e14e4e2e4c8
https://cdn-avatars.huggingface.co/v1/production/uploads/1618772050171-noauth.jpeg
Milan Straka
user
foxik
beeFormer: Bridging the Gap Between Semantic and Interaction Similarity
  in Recommender Systems
Recommender systems often use text-side information to improve their
predictions, especially in cold-start or zero-shot recommendation scenarios,
where traditional collaborative filtering approaches cannot be used. Many
approaches to text-mining side information for recommender systems have been
proposed over recent years, with sentence Transformers being the most prominent
one. However, these models are trained to predict semantic similarity without
utilizing interaction data with hidden patterns specific to recommender
systems. In this paper, we propose beeFormer, a framework for training sentence
Transformer models with interaction data. We demonstrate that our models
trained with beeFormer can transfer knowledge between datasets while
outperforming not only semantic similarity sentence Transformers but also
traditional collaborative filtering methods. We also show that training on
multiple datasets from different domains accumulates knowledge in a single
model, unlocking the possibility of training universal, domain-agnostic
sentence Transformer models to mine text representations for recommender
systems. We release the source code, trained models, and additional details
allowing replication of our experiments at
https://github.com/recombee/beeformer.
BeeFormer enhances sentence Transformers with interaction data, improving recommendation performance across datasets and domains compared to semantic similarity models and traditional collaborative filtering.
sentence Transformers beeFormer interaction data semantic similarity collaborative filtering universal domain-agnostic