Unified Generative Search and Recommendation

• URL: http://arxiv.org/abs/2504.05730v2
• Date: Thu, 10 Apr 2025 06:34:28 GMT
• Title: Unified Generative Search and Recommendation
• Authors: Teng Shi, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Yang Song, Enyun Yu,
• Abstract summary: We introduce GenSAR, a unified generative framework for balanced search and recommendation.<n>Our approach designs dual-purpose identifiers and tailored training strategies to incorporate complementary signals and align with task-specific objectives.<n>Experiments on both public and commercial datasets demonstrate that GenSAR effectively reduces the trade-off and achieves state-of-the-art performance on both tasks.
• Score: 14.317849340141919
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern commercial platforms typically offer both search and recommendation functionalities to serve diverse user needs, making joint modeling of these tasks an appealing direction. While prior work has shown that integrating search and recommendation can be mutually beneficial, it also reveals a performance trade-off: enhancements in one task often come at the expense of the other. This challenge arises from their distinct information requirements: search emphasizes semantic relevance between queries and items, whereas recommendation depends more on collaborative signals among users and items. Effectively addressing this trade-off requires tackling two key problems: (1) integrating both semantic and collaborative signals into item representations, and (2) guiding the model to distinguish and adapt to the unique demands of search and recommendation. The emergence of generative retrieval with Large Language Models (LLMs) presents new possibilities. This paradigm encodes items as identifiers and frames both search and recommendation as sequential generation tasks, offering the flexibility to leverage multiple identifiers and task-specific prompts. In light of this, we introduce GenSAR, a unified generative framework for balanced search and recommendation. Our approach designs dual-purpose identifiers and tailored training strategies to incorporate complementary signals and align with task-specific objectives. Experiments on both public and commercial datasets demonstrate that GenSAR effectively reduces the trade-off and achieves state-of-the-art performance on both tasks.

Related papers

• Unifying Search and Recommendation: A Generative Paradigm Inspired by Information Theory [25.70711328738117]
  GenSR is a novel generative paradigm for unifying search and recommendation. Our work introduces a new generative paradigm compared with previous discriminative methods.
  arXiv  Detail & Related papers  (2025-04-09T09:15:37Z)
• DOGR: Leveraging Document-Oriented Contrastive Learning in Generative Retrieval [10.770281363775148]
  We propose a novel and general generative retrieval framework, namely Leveraging Document-Oriented Contrastive Learning in Generative Retrieval (DOGR)<n>It adopts a two-stage learning strategy that captures the relationship between queries and documents comprehensively through direct interactions.<n>Negative sampling methods and corresponding contrastive learning objectives are implemented to enhance the learning of semantic representations.
  arXiv  Detail & Related papers  (2025-02-11T03:25:42Z)
• Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other? [9.215695600542249]
  Generative retrieval for search and recommendation is a promising paradigm for retrieving items. These generative systems can play a crucial role in centralizing a variety of Information Retrieval (IR) tasks in a single model. This paper investigates whether and when such a unified approach can outperform task-specific models in the IR tasks of search and recommendation.
  arXiv  Detail & Related papers  (2024-10-22T08:49:43Z)
• Generative Retrieval Meets Multi-Graded Relevance [104.75244721442756]
  We introduce a framework called GRaded Generative Retrieval (GR$2$) GR$2$ focuses on two key components: ensuring relevant and distinct identifiers, and implementing multi-graded constrained contrastive training. Experiments on datasets with both multi-graded and binary relevance demonstrate the effectiveness of GR$2$.
  arXiv  Detail & Related papers  (2024-09-27T02:55:53Z)
• 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)
• EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration [63.112790050749695]
  We introduce EAGER, a novel generative recommendation framework that seamlessly integrates both behavioral and semantic information. We validate the effectiveness of EAGER on four public benchmarks, demonstrating its superior performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-06-20T06:21:56Z)
• A Survey of Generative Search and Recommendation in the Era of Large Language Models [125.26354486027408]
  generative search (retrieval) and recommendation aims to address the matching problem in a generative manner. Superintelligent generative large language models have sparked a new paradigm in search and recommendation.
  arXiv  Detail & Related papers  (2024-04-25T17:58:17Z)
• PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement [59.6260680005195]
  We present a novel Person Search framework based on the Diffusion model, PSDiff.<n> PSDiff formulates the person search as a dual denoising process from noisy boxes and ReID embeddings to ground truths.<n>Following the new paradigm, we further design a new Collaborative Denoising Layer (CDL) to optimize detection and ReID sub-tasks in an iterative and collaborative way.
  arXiv  Detail & Related papers  (2023-09-20T08:16:39Z)
• Compositional Attention: Disentangling Search and Retrieval [66.7108739597771]
  Multi-head, key-value attention is the backbone of the Transformer model and its variants. Standard attention heads learn a rigid mapping between search and retrieval. We propose a novel attention mechanism, called Compositional Attention, that replaces the standard head structure.
  arXiv  Detail & Related papers  (2021-10-18T15:47:38Z)
• USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence [36.91974576050925]
  We argue that jointly modeling these two tasks will benefit both of them and finally improve overall user satisfaction. We propose first integrating the user's behaviors in search and recommendation into a heterogeneous behavior sequence, then utilizing a joint model for handling both tasks.
  arXiv  Detail & Related papers  (2021-09-30T11:06:15Z)

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.