The paper introduces Agentic Share-of-Search (ASoS), a multi‑agent AI system designed to aid sellers in competitive decision‑making within large‑language‑model (LLM) mediated e‑commerce. It automates competitive visibility measurement and root‑cause diagnosis by deploying query agents on leading AI platforms and employing a ReAct‑based diagnostic agent to suggest prioritized merchandising actions. A 100‑trial ablation study demonstrates the prototype’s effectiveness, recovering the ablated signal in 39% of trials (95% CI: 30.0%‑48.8%) and 63.9% in high‑correlation cases, outperforming chance by 5.5×.
By Spandan Ghose Chowdhury
arXiv:2605.14542v2 Announce Type: replace
Abstract: A skilled live-commerce host is not merely a narrator, but a sales agent who converts viewer curiosity into purchase intent through expert product...
By Yuyan Chen
arXiv:2606. 12924v1 Announce Type: new Abstract: We present a modular two-agent simulation framework for evaluating conversational shopping assistant architectures.
By Jetlir Duraj, Jayanth Yetukuri, Shuang Zhou, Dhruv Varma, Rui Kong, Ishita Khan, Qunzhi Zhou
PersonaMem-v3 is a benchmark and evaluation harness designed to assess omni-platform personal intelligence for AI agents. It is built from over one million anonymized real-world engagement histories, covering social media, chatbots, calendars, and AI companions, and tracks user preferences and habits over time. The benchmark tests agents on personalization, LLM-powered recommendation, proactiveness, agentic tool use, and geo-temporal reasoning, evaluating their ability to infer holistic user understanding, personalize responses, rerank recommendations, follow user steering, and avoid inappropriate personalization.
By Bowen Jiang, Yuan Yuan, Zhuoqun Hao, Yuchen Liu, Maohao Shen, Sihao Chen, Gregory Wornell, Chris Callison-Burch, Lyle Ungar, Dan Roth, Qi Guo, Xiangjun Fan, Camillo J. Taylor, Hanchao Yu
arXiv:2608. 06632v1 Announce Type: new Abstract: Industrial recommendation systems predominantly adopt a passive ranking paradigm that infers user preferences from implicit behavioral signals (e.
By Ziyun Xu, Bosen Ding, Yue Zhang, Ji Qi, Qingyuan Song, Jizhou Huang, Liwei Wang, Jefferey Santelli, Yue Weng, Qichao Que, Zhenheng Yang, Junfeng Pan, Linhong Zhu
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
arXiv:2609.18729v1 Announce Type: cross
Abstract: Consumers increasingly use AI chatbots for advice on what to buy. With companies like OpenAI and Google monetising their AI through advertising, this...
By Lucas G. Uberti-Bona Marin, Thales Bertaglia, Giovanni Astante, Bram Rijsbosch, Gijs van Dijck, Anik\'o Hann\'ak, Gerasimos Spanakis, Konrad Kollnig
The paper presents a pipeline for generating multi‑turn synthetic conversations and a self‑improvement loop that uses variance‑based contrastive optimization and a coding agent to refine planning and tool‑use in conversational recommendation agents. This approach improves agent quality by 8% over a manually optimized prompt and has been deployed at Spotify, where it accelerated development cycles. In production, the system achieved a 14% increase in user listening, a 5% rise in weekly active users, and a 5% reduction in skip rate compared to a prior session‑only experience.
By Enrico Palumbo, Alexandre Tamborrino, Victor Ode, Ben Lacker, Adri\`a Casas Escoda, Jeremy Hopple, Marcus Better, James Leoni, Hugo Galv\~ao, Hugues Bouchard, Mounia Lalmas, Jos\'e Luis Redondo Garc\'ia, Abenezer Abebe, Ann Clifton, Anton Blomberg, Henrik Lindstr\"om, Dani Doro, Christine Doig Cardet
arXiv:2608. 04625v1 Announce Type: new Abstract: Industrial recommendation strategy iteration heavily relies on large-scale A/B experimentation.
By Zhuohang Jiang, Yuxin Chen, Yongsen Pan, Zheng Hu, Wenqi Fan, Qing Li, Hongyang Wang, Jun Wang, Wenwu Ou
How and why does a recommender system fail the users it serves? Oftentimes, practitioners are left to improve their algorithms based on a combination of feedback from stakeholder teams, domain experti...
arXiv:2602. 10226v2 Announce Type: replace-cross Abstract: Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyperparameter search space and, more critically, designing sophisticated optimizers, architectures, and reward functions to capture nuanced user behaviors.
By Haochen Wang, Yi Wu, Daryl Chang, Li Wei, Lukasz Heldt
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