arXiv:2606. 17698v1 Announce Type: new Abstract: As LLM-based shopping agents enter production, existing benchmarks fail to capture how a shopper's requirements arrive: stated implicitly in the query, recorded in a profile, or revealed only when the right question is asked.
By Zeyao Du, Tong Li, Haibo Zhang
The paper proposes a single hierarchical Semantic ID (SID) system to unify product identification across multiple merchants in e-commerce. By learning SID representations from product content, the authors demonstrate that ranking algorithms can aggregate consumer affinity and product performance over SID prefixes, improving offline relevance and online engagement. For query reformulation, SID concepts guide navigation and refinement, yielding better intent preservation and higher-quality suggestions compared to taxonomy or raw query transitions.
By Steven Xu, Sanjyot Thete, Saathvik Dirisala, Raghav Saboo, Nimesh Sinha, Leo Shao, Elyse Winer, Sudeep Das, Martin Wang, Kyle MacDonald
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
arXiv:2608. 05212v1 Announce Type: new Abstract: Deep search agents tackle challenging questions through long-horizon web interactions, a process that is both complex and fragile: small reasoning errors may propagate through long, noisy trajectories into fluent but incorrect answers.
By Zhixiang Liang, Yifei Liu, Yidan Huang, Haozhe Zhao, Beichen Huang, Jiaqi Wang, Nan Duan, Qiong Cao
RPTune is an end‑to‑end framework that improves in‑context catalog search for small merchant businesses by learning to curate product catalogs and fine‑tuning large language models (LLMs) with catalog‑grounded supervision. It uses an encoder‑reorganizer curator to order and prune products based on LLM feedback, and then applies context‑relative rewards during LLM post‑training. Across seven real merchants and 100 complex conversational queries per merchant, RPTune boosts search accuracy by up to 31.4 percentage points from curation alone and an additional 10.3 points on average from post‑training.
By Chuxuan Hu, Hejie Cui, Norman Huang, Shubham Kumar Bharti, Wang-Chiew Tan, Sercan \"O. Ar{\i}k
The paper introduces a lifecycle framework for LLM-as-a-Judge systems used to evaluate recommendation explanations at Netflix. It outlines four phases—Birth, Training, Deployment, and Monitoring—detailing how each stage addresses specific technical and operational challenges. The authors report that after five weeks of A/B testing, judge-aligned explanations increased novel content viewing and successful browse-to-play sessions without quality takedowns.
By Emma Yanyang Kong, JJ Tan, Ishan Gupta, Lars Olds, Claire Campbell, David Fagnan, Veli Balin, Rohan Gosain, Louis Garcia, Minsu Jang