IntSR: An Integrated Generative Framework for Search and Recommendation

• URL: http://arxiv.org/abs/2509.21179v2
• Date: Fri, 26 Sep 2025 05:16:49 GMT
• Title: IntSR: An Integrated Generative Framework for Search and Recommendation
• Authors: Huimin Yan, Longfei Xu, Junjie Sun, Ni Ou, Wei Luo, Xing Tan, Ran Cheng, Kaikui Liu, Xiangxiang Chu,
• Abstract summary: IntSR is an integrated generative framework for search and recommendation.<n>It integrates these disparate tasks using distinct query modalities.<n>IntSR has been successfully deployed across various scenarios in Amap.
• Score: 23.773378364635523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative recommendation has emerged as a promising paradigm, demonstrating remarkable results in both academic benchmarks and industrial applications. However, existing systems predominantly focus on unifying retrieval and ranking while neglecting the integration of search and recommendation (S&R) tasks. What makes search and recommendation different is how queries are formed: search uses explicit user requests, while recommendation relies on implicit user interests. As for retrieval versus ranking, the distinction comes down to whether the queries are the target items themselves. Recognizing the query as central element, we propose IntSR, an integrated generative framework for S&R. IntSR integrates these disparate tasks using distinct query modalities. It also addresses the increased computational complexity associated with integrated S&R behaviors and the erroneous pattern learning introduced by a dynamically changing corpus. IntSR has been successfully deployed across various scenarios in Amap, leading to substantial improvements in digital asset's GMV(+9.34%), POI recommendation's CTR(+2.76%), and travel mode suggestion's ACC(+7.04%).

Related papers

• RAGRouter-Bench: A Dataset and Benchmark for Adaptive RAG Routing [37.7721677767453]
  We introduce RAG-Bench, the first dataset and benchmark designed for adaptive RAG routing.<n>RAG-Bench revisits retrieval from a query-corpus compatibility perspective and standardizes five representative RAG paradigms for systematic evaluation.<n> Experiments with DeepSeek-V3 and LLaMA-3.1-8B demonstrate that no single RAG paradigm is universally optimal.
  arXiv  Detail & Related papers  (2026-01-30T20:38:11Z)
• When should I search more: Adaptive Complex Query Optimization with Reinforcement Learning [26.489185170468062]
  We propose a novel RL framework called Adaptive Complex Query Optimization (ACQO)<n>Our framework is designed to adaptively determine when and how to expand the search process.<n>ACQO achieves state-of-the-art performance on three complex query benchmarks, significantly outperforming established baselines.
  arXiv  Detail & Related papers  (2026-01-29T03:16:53Z)
• Test-time Corpus Feedback: From Retrieval to RAG [21.517949407443453]
  Retrieval-Augmented Generation (RAG) has emerged as a standard framework for knowledge-intensive NLP tasks.<n>Most RAG pipelines treat retrieval and reasoning as isolated components, retrieving documents once and then generating answers without further interaction.<n>Recent work in both the information retrieval (IR) and NLP communities has begun to close this gap by introducing adaptive retrieval and ranking methods that incorporate feedback.
  arXiv  Detail & Related papers  (2025-08-21T10:57:38Z)
• Enhancing Retrieval-Augmented Generation for Electric Power Industry Customer Support [0.5277756703318045]
  This case study evaluates recent techniques for building a robust customer support system in the electric power domain.<n>We find that query rewriting improves retrieval for queries using non-standard terminology.<n>RAG Fusion boosts performance on vague or multifaceted queries by merging multiple retrievals.<n>Intent recognition supports the decomposition of complex questions into more targeted sub-queries.
  arXiv  Detail & Related papers  (2025-08-01T08:02:23Z)
• HawkBench: Investigating Resilience of RAG Methods on Stratified Information-Seeking Tasks [102.72071523999169]
  HawkBench is a human-labeled, multi-domain benchmark designed to rigorously assess RAG performance.<n>By stratifying tasks based on information-seeking behaviors, HawkBench provides a systematic evaluation of how well RAG systems adapt to diverse user needs.
  arXiv  Detail & Related papers  (2025-02-19T06:33:39Z)
• Unanswerability Evaluation for Retrieval Augmented Generation [74.3022365715597]
  UAEval4RAG is a framework designed to evaluate whether RAG systems can handle unanswerable queries effectively.<n>We define a taxonomy with six unanswerable categories, and UAEval4RAG automatically synthesizes diverse and challenging queries.
  arXiv  Detail & Related papers  (2024-12-16T19:11:55Z)
• SiReRAG: Indexing Similar and Related Information for Multihop Reasoning [96.60045548116584]
  SiReRAG is a novel RAG indexing approach that explicitly considers both similar and related information.<n>SiReRAG consistently outperforms state-of-the-art indexing methods on three multihop datasets.
  arXiv  Detail & Related papers  (2024-12-09T04:56:43Z)
• IRSC: A Zero-shot Evaluation Benchmark for Information Retrieval through Semantic Comprehension in Retrieval-Augmented Generation Scenarios [14.336896748878921]
  This paper introduces the IRSC benchmark for evaluating the performance of embedding models in multilingual RAG tasks. The benchmark encompasses five retrieval tasks: query retrieval, title retrieval, part-of-paragraph retrieval, keyword retrieval, and summary retrieval. Our contributions include: 1) the IRSC benchmark, 2) the SSCI and RCCI metrics, and 3) insights into the cross-lingual limitations of embedding models.
  arXiv  Detail & Related papers  (2024-09-24T05:39:53Z)
• Unified Active Retrieval for Retrieval Augmented Generation [69.63003043712696]
  In Retrieval-Augmented Generation (RAG), retrieval is not always helpful and applying it to every instruction is sub-optimal. Existing active retrieval methods face two challenges: 1. They usually rely on a single criterion, which struggles with handling various types of instructions. They depend on specialized and highly differentiated procedures, and thus combining them makes the RAG system more complicated.
  arXiv  Detail & Related papers  (2024-06-18T12:09:02Z)
• When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation [56.98380787425388]
  We propose a search-Enhanced framework for the Sequential Recommendation (SESRec) SESRec disentangling similar and dissimilar representations within S&R behaviors. Experiments on both industrial and public datasets demonstrate that SESRec consistently outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2023-05-18T09:04:50Z)
• Generation-Augmented Retrieval for Open-domain Question Answering [134.27768711201202]
  Generation-Augmented Retrieval (GAR) for answering open-domain questions. We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader.
  arXiv  Detail & Related papers  (2020-09-17T23:08:01Z)

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.