Recommender Systems with Generative Retrieval
- URL: http://arxiv.org/abs/2305.05065v3
- Date: Fri, 3 Nov 2023 18:02:56 GMT
- Title: Recommender Systems with Generative Retrieval
- Authors: Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan H. Keshavan,
Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Samost,
Maciej Kula, Ed H. Chi, Maheswaran Sathiamoorthy
- Abstract summary: We propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates.
To that end, we create semantically meaningful of codewords to serve as a Semantic ID for each item.
We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets.
- Score: 58.454606442670034
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Modern recommender systems perform large-scale retrieval by first embedding
queries and item candidates in the same unified space, followed by approximate
nearest neighbor search to select top candidates given a query embedding. In
this paper, we propose a novel generative retrieval approach, where the
retrieval model autoregressively decodes the identifiers of the target
candidates. To that end, we create semantically meaningful tuple of codewords
to serve as a Semantic ID for each item. Given Semantic IDs for items in a user
session, a Transformer-based sequence-to-sequence model is trained to predict
the Semantic ID of the next item that the user will interact with. To the best
of our knowledge, this is the first Semantic ID-based generative model for
recommendation tasks. We show that recommender systems trained with the
proposed paradigm significantly outperform the current SOTA models on various
datasets. In addition, we show that incorporating Semantic IDs into the
sequence-to-sequence model enhances its ability to generalize, as evidenced by
the improved retrieval performance observed for items with no prior interaction
history.
Related papers
- SC-Rec: Enhancing Generative Retrieval with Self-Consistent Reranking for Sequential Recommendation [18.519480704213017]
We propose SC-Rec, a unified recommender system that learns diverse preference knowledge from two distinct item indices and multiple prompt templates.
SC-Rec considerably outperforms the state-of-the-art methods for sequential recommendation, effectively incorporating complementary knowledge from varied outputs of the model.
arXiv Detail & Related papers (2024-08-16T11:59:01Z) - Generative Retrieval with Preference Optimization for E-commerce Search [16.78829577915103]
We develop an innovative framework for E-commerce search, called generative retrieval with preference optimization.
We employ multi-span identifiers to represent raw item titles and transform the task of generating titles from queries into the task of generating multi-span identifiers from queries.
Our experiments show that this framework achieves competitive performance on a real-world dataset, and online A/B tests demonstrate the superiority and effectiveness in improving conversion gains.
arXiv Detail & Related papers (2024-07-29T09:31:19Z) - ACE: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling [53.97609687516371]
We propose a pioneering generAtive Cross-modal rEtrieval framework (ACE) for end-to-end cross-modal retrieval.
ACE achieves state-of-the-art performance in cross-modal retrieval and outperforms the strong baselines on Recall@1 by 15.27% on average.
arXiv Detail & Related papers (2024-06-25T12:47:04Z) - MMGRec: Multimodal Generative Recommendation with Transformer Model [81.61896141495144]
MMGRec aims to introduce a generative paradigm into multimodal recommendation.
We first devise a hierarchical quantization method Graph CF-RQVAE to assign Rec-ID for each item from its multimodal information.
We then train a Transformer-based recommender to generate the Rec-IDs of user-preferred items based on historical interaction sequences.
arXiv Detail & Related papers (2024-04-25T12:11:27Z) - Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential
Recommendations [50.03560306423678]
We propose Ada-Retrieval, an adaptive multi-round retrieval paradigm for recommender systems.
Ada-Retrieval iteratively refines user representations to better capture potential candidates in the full item space.
arXiv Detail & Related papers (2024-01-12T15:26:40Z) - MISSRec: Pre-training and Transferring Multi-modal Interest-aware
Sequence Representation for Recommendation [61.45986275328629]
We propose MISSRec, a multi-modal pre-training and transfer learning framework for sequential recommendation.
On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests.
On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation.
arXiv Detail & Related papers (2023-08-22T04:06:56Z) - Text Summarization with Latent Queries [60.468323530248945]
We introduce LaQSum, the first unified text summarization system that learns Latent Queries from documents for abstractive summarization with any existing query forms.
Under a deep generative framework, our system jointly optimize a latent query model and a conditional language model, allowing users to plug-and-play queries of any type at test time.
Our system robustly outperforms strong comparison systems across summarization benchmarks with different query types, document settings, and target domains.
arXiv Detail & Related papers (2021-05-31T21:14:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.