title: Item-Language Model for Conversational Recommendation
summary: Large-language Models (LLMs) have been extremely successful at tasks like
  complex dialogue understanding, reasoning and coding due to their emergent
  abilities. These emergent abilities have been extended with multi-modality to
  include image, audio, and video capabilities. Recommender systems, on the other
  hand, have been critical for information seeking and item discovery needs.
  Recently, there have been attempts to apply LLMs for recommendations. One
  difficulty of current attempts is that the underlying LLM is usually not
  trained on the recommender system data, which largely contains user interaction
  signals and is often not publicly available. Another difficulty is user
  interaction signals often have a different pattern from natural language text,
  and it is currently unclear if the LLM training setup can learn more
  non-trivial knowledge from interaction signals compared with traditional
  recommender system methods. Finally, it is difficult to train multiple LLMs for
  different use-cases, and to retain the original language and reasoning
  abilities when learning from recommender system data. To address these three
  limitations, we propose an Item-Language Model (ILM), which is composed of an
  item encoder to produce text-aligned item representations that encode user
  interaction signals, and a frozen LLM that can understand those item
  representations with preserved pretrained knowledge. We conduct extensive
  experiments which demonstrate both the importance of the language-alignment and
  of user interaction knowledge in the item encoder.
paper_authors._id: 66612192754f90a85825e701
paper_authors.name: Li Yang
paper_authors.status: claimed_verified
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paper_authors.fullname: Li Yang
paper_authors.type: user
paper_authors.user: liyang2019
paper_authors._id: 66612192754f90a85825e702
paper_authors.name: Anushya Subbiah
paper_authors._id: 66612192754f90a85825e703
paper_authors.name: Hardik Patel
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-06-07T13:44:23.493Z
paper_authors._id: 63a850b32e05ca32e34b4cf9
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paper_authors.fullname: Hardik Patel
paper_authors.type: user
paper_authors.user: hardikp
paper_authors._id: 66612192754f90a85825e704
paper_authors.name: Judith Yue Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-06T10:19:45.590Z
paper_authors._id: 5f431c9f79c1ba4c353d0d7f
paper_authors.avatarUrl: /avatars/7ac0936f469e63cfe8ce15849e128a43.svg
paper_authors.fullname: Judith Yue Li
paper_authors.type: user
paper_authors.user: whereisdodo
paper_authors._id: 66612192754f90a85825e705
paper_authors.name: Yanwei Song
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-06T10:19:57.540Z
paper_authors._id: 63917e92b6b839bb61484963
paper_authors.avatarUrl: /avatars/1cf4b573cf28b3649776f6057d0aaf8c.svg
paper_authors.fullname: Song Yanwei
paper_authors.type: user
paper_authors.user: songyw9
paper_authors._id: 66612192754f90a85825e706
paper_authors.name: Reza Mirghaderi
paper_authors._id: 66612192754f90a85825e707
paper_authors.name: Vikram Aggarwal
paper_title: Item-Language Model for Conversational Recommendation
paper_summary: Large-language Models (LLMs) have been extremely successful at tasks like
  complex dialogue understanding, reasoning and coding due to their emergent
  abilities. These emergent abilities have been extended with multi-modality to
  include image, audio, and video capabilities. Recommender systems, on the other
  hand, have been critical for information seeking and item discovery needs.
  Recently, there have been attempts to apply LLMs for recommendations. One
  difficulty of current attempts is that the underlying LLM is usually not
  trained on the recommender system data, which largely contains user interaction
  signals and is often not publicly available. Another difficulty is user
  interaction signals often have a different pattern from natural language text,
  and it is currently unclear if the LLM training setup can learn more
  non-trivial knowledge from interaction signals compared with traditional
  recommender system methods. Finally, it is difficult to train multiple LLMs for
  different use-cases, and to retain the original language and reasoning
  abilities when learning from recommender system data. To address these three
  limitations, we propose an Item-Language Model (ILM), which is composed of an
  item encoder to produce text-aligned item representations that encode user
  interaction signals, and a frozen LLM that can understand those item
  representations with preserved pretrained knowledge. We conduct extensive
  experiments which demonstrate both the importance of the language-alignment and
  of user interaction knowledge in the item encoder.
paper_ai_keywords: Large-language Models (LLMs) multi-modality image audio video recommender systems user interaction signals Item-Language Model (ILM) item encoder text-aligned item representations