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
arXiv:2606. 26277v1 Announce Type: cross Abstract: Sequential user behavior modeling is widely adopted in industrial recommender systems; however, significant gaps remain in financial services, where pre-login web interactions and authenticated in-app experiences differ drastically.
By Dianjing Fan, Yao Li, Kyaw Hpone Myint, Dwipam Katariya, Alexandre G. R. Day, Pranab Mohanty, Giri Iyengar
BehaveRec is a three-signal framework that combines a causal session transformer, a bidirectional User History Encoder over a 90‑day window, and a GraphSAGE module built from behavioral embedding similarity. On an industrial dataset of 137M sequences, it improves R@5 by +3.2% over SASRec, and its advantage grows with user history length, becoming significant for users with 101+ interactions. The study also establishes a practical deployment rule of roughly 20 interactions per user for when long‑term history and behavioral‑graph signals are worthwhile.
By Aysenur Inan, Reza Yousefi Maragheh, Jayesh Uddhav Kudase, Selene Xu, Kaushiki Nag, Jianpeng Xu, Sushant Kumar
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
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:2609.08622v1 Announce Type: cross
Abstract: Predictive process monitoring aims at forecasting various aspects of running processes. Among the different tasks, next activity prediction represent...
By Alessandro Mele, Claudia Diamantini, Domenico Potena
arXiv:2608. 06752v1 Announce Type: new Abstract: This paper proposes DKG-MTI, a dual knowledge graph framework for unified multi-task user intent inference from online travel reviews.
By Tzu-Cheng Peng (National Taiwan University), Chien Chin Chen (National Taiwan University), Chih-Hao Ku (University of North Texas), Yung-Chun Chang (Taipei Medical University)
arXiv:2604. 25834v2 Announce Type: replace Abstract: With the rapid development of the Internet, users have increasingly higher expectations for the recommendation accuracy of online content consumption platforms.
By Wenhao Li, Zihan Lin, Zhengxiao Guo, Jie Zhou, Shukai Liu, Yongqi Liu, Chuan Luo, Chaoyi Ma, Ruiming Tang, Han Li
arXiv:2607. 07974v1 Announce Type: cross Abstract: Intent detection is a critical task that bridges human intents and system actions in human-machine interaction systems.
By Yihong Xu, Mingyu Kang, Linyuan L\"u
The paper introduces an uncertainty‑aware probabilistic framework for continual discovery of new user intents in open‑world settings where the label space evolves. It encodes utterances with an adaptive β‑VAE, uses classifier confidence, posterior uncertainty, and DP‑GMM likelihood to separate known from novel intents, and promotes only reliable clusters to new labels while employing replay and Elastic Weight Consolidation to avoid forgetting. Experiments demonstrate high novelty precision, stable adaptation across phases, and limited forgetting, with qualitative results showing coherent semantic clusters despite conservative label expansion.
By Pisante Aida, Formentin Simone
arXiv:2607. 01785v1 Announce Type: new Abstract: Next activity prediction helps service-oriented processes anticipate upcoming steps before delays, exceptions, or service-level risks occur.
By Jiaxing Wang, Kaitao Chen, Zhubin Han, Chenyu Hou, Bin Cao, Jing Fan, Ji Zhang
arXiv:2608. 15429v1 Announce Type: new Abstract: Prior embedding models for sequential recommendation typically operate within a homogeneous action space, limiting their ability to capture cross-surface behavioral signals spanning distinct behavioral domains.
By Tsz Fung Pang, Po Jen Chen, Nimish Ronghe, Farhad Farahani, Bo Zhang