The paper introduces the Behavior-to-Value (B2V) task, which seeks to identify consumer values from e-commerce behavioral trajectories. It presents the E-commerce Consumption Value Taxonomy (ECVT) and the B2V-Bench dataset, derived from anonymized Taobao logs and covering 25 purchase behaviors with associated value orientations. A new model, B2V-Verifier, is proposed to improve value measurement accuracy, achieving a 34% boost in multi-label classification over strong LLM baselines.
By Peixuan Hou, Bin Chen, Li He, Jian Xu, Bo Zheng, Xiuli Ma, Guojie Song
arXiv:2607. 14161v1 Announce Type: cross Abstract: Pinterest is where people turn inspiration into action as users browse ideas, then take steps toward realization, often by discovering shoppable content.
By Junpeng Hou, XianXing Zhang, Sai Xiao, Derek Cheng, Darren Reger, Olafur Gudmundsson, Mehdi Ben Ayed, Zhiqing Rao, Huizhong Duan
arXiv:2606. 02798v1 Announce Type: new Abstract: Many decision-support settings require systems that adapt to individual users, but evaluation data for this problem remain limited.
By Liangwei Yang, Jielin Qiu, Zixiang Chen, Ming Zhu, Juntao Tan, Zhiwei Liu, Wenting Zhao, Zhujun Lan, Akshara Prabhakar, Silvio Savarese, Huan Wang, Shelby Heinecke
The paper introduces TRACER, a multi‑turn user simulator that models evolving user intent and aligns simulated behavior with real interaction trajectories. TRACER is trained first with supervised fine‑tuning on real dialogues and then with reinforcement learning that uses hierarchical outcome‑ and trajectory‑level rewards to address reward sparsity and credit assignment. In real customer‑service sessions, TRACER‑7B outperforms the best baseline by 11.4 conversion F1, achieves the lowest group‑level conversion‑rate error and semantic trajectory distance, and generalizes to out‑of‑distribution scenarios, while human Turing tests show its conversations appear natural. The authors also present the Dynamic Marketing Benchmark, which evaluates both persuasion effectiveness and response quality of large language models through simulated interactions, demonstrating that higher response quality does not always lead to higher conversion rates.
By Geng Chen, Ruotong Pan, Zhirui Yang, Qiqi He, Jiawei Chen, Zhang Yunfei, Chongyuan Chen, Minxuan Lv, Zheng Yang, Win-Bin Huang, Xiangyu Wu, Wenwu Ou
arXiv:2606.03371v4 Announce Type: replace
Abstract: Reliable proactive agents must choose an action and judge whether current evidence is sufficient to act. We study retail service from sparse third-...
By Honghui Zhang, Anna Min, Chenmeinian Guo, Yujia Zhang, Yichen Yu, Zezhou Zhang, Guanyu Liu, Yongming Qin, Chongguo Song, Mengyue Yang, Lei Yu, Tianyu Shi
arXiv:2607. 20228v1 Announce Type: new Abstract: We propose a hybrid approach for user-centric modeling of transactional event sequences that combines contrastive representation learning (CoLES) with State Space Models (SSMs).
By Ivan Palagin