title: beeFormer: Bridging the Gap Between Semantic and Interaction Similarity
    in Recommender Systems
summary: 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.
paper_authors._id: 66e939a1218585b91b9c81d6
paper_authors.name: Vojtěch Vančura
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-17T09:30:33.019Z
paper_authors._id: 6641d8f7f448effbae1a32ca
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6641d8f7f448effbae1a32ca/mgT3E_Q9rWgHGpeLKHex7.png
paper_authors.fullname: Vojtěch Vančura
paper_authors.type: user
paper_authors.user: beeformer
paper_authors._id: 66e939a1218585b91b9c81d7
paper_authors.name: Pavel Kordík
paper_authors._id: 66e939a1218585b91b9c81d8
paper_authors.name: Milan Straka
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T11:34:56.923Z
paper_authors._id: 607c806188160e14e4e2e4c8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1618772050171-noauth.jpeg
paper_authors.fullname: Milan Straka
paper_authors.type: user
paper_authors.user: foxik
paper_title: beeFormer: Bridging the Gap Between Semantic and Interaction Similarity
    in Recommender Systems
paper_summary: 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.
paper_ai_keywords: sentence Transformers beeFormer semantic similarity recommender Systems collaborative filtering interaction data domain-agnostic text representations