arXiv:2609.38397v1 Announce Type: new
Abstract: Virtual clients offer a cost-effective approach to support applications such as A/B testing, recommender system development, and interface evaluation....
By Yunan Lu, Shuang Xie, Meghna Allamudi, Mingyu Zhao, Han Li, Lingyun Wang, Zhou Yu
arXiv:2511. 08378v4 Announce Type: replace-cross Abstract: Session-based recommendation (SBR) aims to predict anonymous users' next interaction based on their interaction sessions.
By Xiao Wang, Ke Qin, Dongyang Zhang, Xiurui Xie, Shuang Liang
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
The paper introduces CM-PTM, a Cross Multi-source Behavior Pre-Training Model designed to learn mobile game user representations from device-level behavioral logs. It uses hierarchical cascaded mask‑then‑predict tasks to first identify the source of the next behavior and then refine predictions at the app‑action level, thereby modeling cross‑source dependencies and fine‑grained dynamics. Experiments on large real‑world mobile datasets show that CM‑PTM captures users’ endogenous interests and improves performance on downstream mobile game recommendation tasks.
By Chengqi Yang, Yiran Qiao, Feng Liu, Xingyu Lou, Zijun Zhou, Xiaoyun Mo, Changwang Zhang, Jiayuan Xu, Jun Wang, Xiang Ao
MISApp is a profile‑free framework that predicts the next mobile app a user will launch by learning multi‑hop session graphs. It captures transition dependencies across different structural ranges, incorporates temporal context and spatial categorization, and models intent evolution from recent interactions. Experiments on two real‑world datasets show MISApp outperforms baselines in both standard and cold‑start settings while remaining efficient, and analyses reveal that multi‑hop relations provide higher‑order predictive signals and interpretable attention weights.
By Yunchi Yang, Longlong Li, Jianliang Wu, Cunquan Qu
The study examines two behavioral inference tasks—session-level user identification and next-domain prediction—using large-scale anonymous web browsing traces. Classical and neural models are applied to user identification, while graph-based methods combined with Large Language Models (LLMs) are used for next-domain prediction. Results show that short browsing sessions are highly identifiable and future navigation is highly predictable, with LLM-derived semantic features offering only marginal improvements over structural and sequential models.
By Ralph Elsaghbini, Omran Berjawi, Walid Fahs, Rida Khatoun