arXiv AI

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments

arXiv:2602. 23234v4 Announce Type: replace-cross Abstract: Large-scale commercial search systems optimize for relevance to drive successful sessions that help users find what they are looking for.

arXiv AI

Jul 7

Enhancement of E-commerce Sponsored Search Relevancy with LLM

arXiv:2607. 03886v1 Announce Type: cross Abstract: Sponsored search plays a crucial role as a revenue stream for search engines, wherein advertisers competitively bid on keywords that align with the users' search queries.

By Md Omar Faruk Rokon, Andrei Simion, Weizhi Du, Musen Wen, Hong Yao, Kuang-chih Lee

llms fine-tuning safety

arXiv Machine Learning

Jul 28

SMART: LLM-Augmented Hybrid Retrieval for Dynamic Product Ads

arXiv:2607. 23121v1 Announce Type: cross Abstract: Dynamic Product Ads (DPA) require retrieving relevant items from multi-million product catalogs, balancing two competing objectives: retargeting (re-surfacing known interests) and prospecting (discovering new categories).

By Congfei Zhang, Jingxiao Ma, Xiaodong Liu, Hsiang-wei Chao, Siman Wang, Ge Liu, Shantanu Aggarwal, Vincent Zhang, Meghana Missula, Rachel Liao, Zichu Li, Xiao Bai, Yunzhi Zhou, Yajun Wang, Zhe Liu, Jinchao Li, Yu Zhang

llms rag

arXiv AI

Jul 31

Improving Item Discoverability in e-Commerce Search via Related Intent Generation

arXiv:2607. 27172v1 Announce Type: cross Abstract: Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall.

By Ji Xin, Xiao Xiao, Ishan Bhatt, Vinesh Gudla, Trace Levinson, Raochuan Fan, Shishir Kumar Prasad, Prakash Putta, Tejaswi Tenneti

llms fine-tuning efficiency

Hugging Face Trending Papers

Jul 29

Improving Item Discoverability in e-Commerce Search via Related Intent Generation

Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall. In e-commerce marketplaces and particularly grocery, this paradigm is limiting, as user satisfaction and commercial outcomes depend heavily on the discoverability of substitute, complementary, and thematically related items.

llms fine-tuning efficiency

arXiv AI

Jun 3

Do Neural Retrievers Prefer Certain Documents? Evidence of Learned Relevance Priors

arXiv:2606. 02814v1 Announce Type: cross Abstract: Neural retrievers are trained to estimate query-document relevance from annotated query-document pairs.

By Francisco Valentini, Edgar Altszyler, Martin Fajcik

llms rag benchmarks safety

arXiv AI

Jul 3

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

arXiv:2604. 02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation.

By Yuhang Wu, Xiangqing Shen, Fanfan Wang, Cangqi Zhou, Zhen Wu, Xinyu Dai, Rui Xia

llms rag reinforcement-learning benchmarks

arXiv Machine Learning

Jun 30

Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation

arXiv:2606. 29947v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as rerankers in recommender systems, with the expectation that semantic understanding will help in cold-start and long-tail regimes.

By Zhe Dong (University of Maine at Presque Isle), Fang Qin (Stanford University), Manish Shah (Independent Researcher), Yicheng Wang (Independent Researcher)

llms rag benchmarks

arXiv Machine Learning

Aug 4

Advancing Relevance Measurement with Vision-Language Models for Web-Scale Search

arXiv:2608. 02446v1 Announce Type: cross Abstract: Relevance evaluation plays a crucial role in personalized search systems, serving as a guardrail alongside user engagement metrics to ensure that search results align with user queries and intent.

By Han Wang, Alex Whitworth, Pak Ming Cheung, Zhenjie Zhang, Krishna Kamath, Xi Chen, Roberto Konow, Kurchi Subhra Hazra

llms multimodal safety

arXiv AI

Jul 14

Tool-Adaptive LLM Reranker

arXiv:2607. 10555v1 Announce Type: cross Abstract: Generative Large Language Models (LLMs) have revolutionized information retrieval, yet their strictly parametric nature frequently leads to severe factual hallucinations when confronted with complex queries beyond their epistemic boundaries.

By Zichuan Liu, Ruijin Hua

llms rag agents reinforcement-learning efficiency benchmarks safety

arXiv AI

Jun 15

Succeeding at Scale: Enterprise Retrieval Benchmark Construction and Index-Preserving Query Adaptation for Multi-Tenant Search

arXiv:2601. 04646v4 Announce Type: replace-cross Abstract: Large-scale multi-tenant retrieval systems generate extensive query logs but lack curated relevance labels for effective domain adaptation, resulting in substantial underutilized "dark data.

By Prateek Jain, Shabari S Nair, Ritesh Goru, Prakhar Agarwal, Ajay Yadav, Yoga Sri Varshan Varadharajan, Constantine Caramanis

llms fine-tuning benchmarks

arXiv AI

6d ago

CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation

arXiv:2605. 04495v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) relies on evidence ranking to determine what information is exposed to the generator, yet existing retrieval and reranking methods primarily estimate query--document relevance.

By Zhipeng Song, Yizhi Zhou, Xiangyu Kong, Jiulong Jiao, Xuezhou Ye, Chunqi Gao, Xueqing Shi, Yu Wang, Yuhang Zhou, Heng Qi

llms rag

arXiv Machine Learning

Aug 12

UniScale: Synergistic Entire Space Data and Model Scaling for Search Ranking

arXiv:2603. 24226v4 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have inspired a surge of scaling research in industrial search, advertising, and recommendation systems.

By Liren Yu, Caiyuan Li, Feiyi Dong, Tao Zhang, Zhixuan Zhang, Dan Ou, Haihong Tang, Bo Zheng

llms

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