arXiv AI

IntentTune: Using user demand and personalization to resolve "unknown" query intents for e-commerce search

arXiv:2607. 01530v1 Announce Type: cross Abstract: Understanding user intent is fundamental to delivering relevant search results in e-commerce.

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

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

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

Jul 7

APeB: Benchmarking Personalization Ability of Large Language Model Agents

arXiv:2607. 03162v1 Announce Type: new Abstract: LLM-powered agents struggle with personalization when users issue raw, underspecified queries.

By Garry Yang, Zizhe Chen, Xinru Chen, Yongqiang Chen, Jianxiang Wang, Deyu Zou, Linyi Ding, Jialiang Wu, Yunzhong He, Yu Gong, James Cheng, Huaixiao Tou

llms agents benchmarks

arXiv AI

Jun 26

From Clicks to Intent: Cross-Platform Session Embeddings with LLM-Distilled Taxonomy for Financial Services Recommendations

arXiv:2606. 26277v1 Announce Type: cross Abstract: Sequential user behavior modeling is widely adopted in industrial recommender systems; however, significant gaps remain in financial services, where pre-login web interactions and authenticated in-app experiences differ drastically.

By Dianjing Fan, Yao Li, Kyaw Hpone Myint, Dwipam Katariya, Alexandre G. R. Day, Pranab Mohanty, Giri Iyengar

llms rag efficiency multimodal

arXiv AI

Jun 8

Mind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations

arXiv:2606. 06779v1 Announce Type: cross Abstract: In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity for personalization innovation.

By Nimesh Sinha, Raghav Saboo, Martin Wang, Sudeep Das

llms rag

arXiv AI

Jun 18

SAERec: Constructing Fine-grained Interpretable Intents Priors via Sparse Autoencoders for Recommendation

arXiv:2606. 18897v1 Announce Type: cross Abstract: Intent-based recommender systems have gained significant attention for improving accuracy and interpretability by modeling the underlying motivations behind user behaviors.

By Jiangnan Xia, Xuansheng Wu, Yu Yang, Xin Wang, Ninghao Liu

llms rag diffusion benchmarks safety

arXiv AI

Jul 7

Next-Gen Sponsored Search: Crafting the Perfect Query with Inventory-Aware RAG (InvAwr-RAG) Based GenAI

arXiv:2607. 03880v1 Announce Type: cross Abstract: Sponsored search plays a crucial role in e-commerce revenue generation, where advertisers strategically bid on keywords to capture the attention of users through relevant search queries.

By Md Omar Faruk Rokon, Weizhi Du, Zhaodong Wang, Musen Wen

rag

arXiv AI

1d ago

Dear Algo: A Precision-First Agentic Intent Layer for Unified Search and Recommendation

arXiv:2608. 15877v1 Announce Type: new Abstract: Search and recommendation serve a shared discovery objective but encode intent differently.

By Rui Wang, Jiazhou Wang, Zheng Wei, Chenglin Lu, Fangcheng Sun, Ivy Sun, Jin Sun, Hui Geng, Lillian Zhang, Chao Yang, Lei Chen, Shahin Sefati, Reem Helou, Joe Zhou, Babak Shakibi, Yiyi Pan, Bi Xue, Hong Yan, Shujian Bu

llms rag agents multimodal

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

Jun 2

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.

By Evangelia Christakopoulou, Vivekkumar Patel, Hemanth Velaga, Sandip Gaikwad, Sean Suchter, Venkat Sundaranatha

llms fine-tuning

arXiv AI

Jul 21

Pailitao-MMSearch: Building Native E-Commerce Multimodal Search Foundation

arXiv:2607. 17499v1 Announce Type: new Abstract: The evolution of e-commerce has fundamentally transformed how users search for products, shifting from simple text-based keyword queries to complex multimodal interactions that seamlessly combine product images, natural language descriptions, and mixed-intent instructions.

By Xiaohan Ye, Xu Chen, Zihan Gong, Jian Ding, Lianyu Du, Baicheng Chen, Yunmeng Shu, Jingqian Zhao, Zhixiang Zhao, Shuaiqi Jia, Chong Ma, Shuwen Xiao, Xiangheng Kong, Yuan Gao, Jun Song, Jinsong Lan, Xiaoyong Zhu, Bo Zheng

llms multimodal

Related stories