arXiv:2606. 10156v1 Announce Type: cross Abstract: As recommender systems transition toward agentic, multi-turn conversational interfaces, evaluation paradigms have struggled to keep pace.
By Bharath Sivaram Narasimhan, Karthik R Narasimhan
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
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
arXiv:2608. 09282v1 Announce Type: new Abstract: Real-world shopping often requires constructing a basket of complementary items rather than retrieving a single product.
By Adrian Li, Kelong Mao, Yudong Guo, Heming Xia, Xinwei Yang, Lirui Luo, Jace Wong, Pu Yao, Sulong Xu, Simiu Gu
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
User experience is a first-class objective in industrial e-commerce recommender systems (RS). Post-ranking strategies, which govern diversity, similarity, and exposure over a ranked list, are widely deployed in industrial RS for their simplicity and low serving cost.