Recommender Systems with Generative Retrieval
Modern recommender systems leverage large-scale retrieval models consisting
of two stages: training a dual-encoder model to embed queries and candidates in
the same space, followed by an Approximate Nearest Neighbor (ANN) search to
select top candidates given a query's embedding. In this paper, we propose a
new single-stage paradigm: a generative retrieval model which autoregressively
decodes the identifiers for the target candidates in one phase. To do this,
instead of assigning randomly generated atomic IDs to each item, we generate
Semantic IDs: a semantically meaningful tuple of codewords for each item that
serves as its unique identifier. We use a hierarchical method called RQ-VAE to
generate these codewords. Once we have the Semantic IDs for all the items, a
Transformer based sequence-to-sequence model is trained to predict the Semantic
ID of the next item. Since this model predicts the tuple of codewords
identifying the next item directly in an autoregressive manner, it can be
considered a generative retrieval model. We show that our recommender system
trained in this new paradigm improves the results achieved by current SOTA
models on the Amazon dataset. Moreover, we demonstrate that the
sequence-to-sequence model coupled with hierarchical Semantic IDs offers better
generalization and hence improves retrieval of cold-start items for
recommendations.
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Shashank Rajput
claimed_verified
2024-10-01T07:17:13.247Z
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Shashank Rajput
user
shashankMosaicML
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Nikhil Mehta
645b13688b0751914f60b19f
Anima Singh
645b13688b0751914f60b1a0
Raghunandan H. Keshavan
645b13688b0751914f60b1a1
Trung Vu
claimed_verified
2025-06-11T08:41:55.474Z
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Trung Vu
user
trungtvu
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Lukasz Heldt
645b13688b0751914f60b1a3
Lichan Hong
645b13688b0751914f60b1a4
Yi Tay
645b13688b0751914f60b1a5
Vinh Q. Tran
admin_assigned
2023-05-23T09:18:01.499Z
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Vinh Q. Tran
user
vqtran
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Jonah Samost
645b13688b0751914f60b1a7
Maciej Kula
645b13688b0751914f60b1a8
Ed H. Chi
645b13688b0751914f60b1a9
Maheswaran Sathiamoorthy
Recommender Systems with Generative Retrieval
Modern recommender systems leverage large-scale retrieval models consisting
of two stages: training a dual-encoder model to embed queries and candidates in
the same space, followed by an Approximate Nearest Neighbor (ANN) search to
select top candidates given a query's embedding. In this paper, we propose a
new single-stage paradigm: a generative retrieval model which autoregressively
decodes the identifiers for the target candidates in one phase. To do this,
instead of assigning randomly generated atomic IDs to each item, we generate
Semantic IDs: a semantically meaningful tuple of codewords for each item that
serves as its unique identifier. We use a hierarchical method called RQ-VAE to
generate these codewords. Once we have the Semantic IDs for all the items, a
Transformer based sequence-to-sequence model is trained to predict the Semantic
ID of the next item. Since this model predicts the tuple of codewords
identifying the next item directly in an autoregressive manner, it can be
considered a generative retrieval model. We show that our recommender system
trained in this new paradigm improves the results achieved by current SOTA
models on the Amazon dataset. Moreover, we demonstrate that the
sequence-to-sequence model coupled with hierarchical Semantic IDs offers better
generalization and hence improves retrieval of cold-start items for
recommendations.
A generative retrieval model using Semantic IDs and a Transformer sequence-to-sequence approach improves recommendation system performance and generalization, especially for cold-start items.
dual-encoder model Approximate Nearest Neighbor ANN generative retrieval model autoregressive model Semantic IDs codewords RQ-VAE Transformer sequence-to-sequence Amazon dataset cold-start items