Item-Language Model for Conversational Recommendation
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.
66612192754f90a85825e701
Li Yang
claimed_verified
2024-06-07T09:20:31.940Z
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/avatars/74b96b968bd5cb6f3c693ab1966a9353.svg
Li Yang
user
liyang2019
66612192754f90a85825e702
Anushya Subbiah
66612192754f90a85825e703
Hardik Patel
extracted_confirmed
2024-06-07T13:44:23.493Z
63a850b32e05ca32e34b4cf9
/avatars/903d54804ef039137eca13b668ca8331.svg
Hardik Patel
user
hardikp
66612192754f90a85825e704
Judith Yue Li
admin_assigned
2024-06-06T10:19:45.590Z
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/avatars/7ac0936f469e63cfe8ce15849e128a43.svg
Judith Yue Li
user
whereisdodo
66612192754f90a85825e705
Yanwei Song
admin_assigned
2024-06-06T10:19:57.540Z
63917e92b6b839bb61484963
/avatars/1cf4b573cf28b3649776f6057d0aaf8c.svg
Song Yanwei
user
songyw9
66612192754f90a85825e706
Reza Mirghaderi
66612192754f90a85825e707
Vikram Aggarwal
Item-Language Model for Conversational Recommendation
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.
The Item-Language Model integrates user interaction signals into language-aligned item representations to enhance recommendations while preserving pre-trained language abilities.
Large-language Models multi-modality image audio video recommender systems user interaction signals Item-Language Model item encoder language-alignment user interaction knowledge frozen LLM