arXiv Machine Learning

MISApp: Multi-Hop Intent-Aware Session Graph Learning for Next App Prediction

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.

arXiv AI

Sep 2

User Representation via Cross Multi-source Behavior Pre-training for Mobile Games

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

efficiency

arXiv AI

Jun 26

From Clicks to Intent: Cross-Platform Session Embeddings with LLM-Distilled Taxonomy for Financial Services Recommendations

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

llms rag efficiency multimodal

arXiv AI

Jun 19

Bid Farewell to Seesaw: Towards Accurate Long-tail Session-based Recommendation via Dual Constraints of Hybrid Intents

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

benchmarks

arXiv AI

4d ago

Leveraging contextual events on structure-aware next activity prediction

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 AI

Aug 10

Mind the Gap: A Dual Knowledge Graph Framework for Unified Multi-task User Intent Inference

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)

llms safety

arXiv AI

Jul 15

Action-Aware Generative Sequence Modeling for Short Video Recommendation

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 AI

Jul 10

A Multi-cluster Boundary Learning Method for Out-of-Scope Intent Detection via MiniLM Embedding

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

llms rag benchmarks

arXiv Machine Learning

Jul 3

EHHN: An Event-driven Heterogeneous Hypergraph Network for Object-Centric Next Activity Prediction

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

benchmarks

arXiv Machine Learning

Aug 18

SAGA: Structure-Attended Generative Action Embedding Model that encodes Multi-Surface User Action Sequences

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

rag nlp benchmarks

arXiv Machine Learning

Jun 5

Bridging the Semantic-Collaborative Gap: An Asymmetric Graph Architecture for Cold-Start Item Recommendation

arXiv:2606. 06225v1 Announce Type: cross Abstract: Collaborative filtering and graph-based recommendation models are highly effective because they leverage observed user interactions, but this dependence creates a fundamental cold-start challenge when newly added content has no interaction history.

By Anh Truong, John Trenkle, Yuanbo Chen, Honghong Zhao, Abdullah Alchihabi, Effy Fang, Michael Tamir

rag

arXiv AI

Aug 6

Spatiotemporal Graph Transformer for Traffic Intelligence in Edge Computing

arXiv:2608. 04075v1 Announce Type: cross Abstract: Accurate traffic forecasting is essential for proactive resource management in edge computing, where service demand evolves dynamically across both space and time.

By Laha Ale, Letian Lin, Na Cao, Zheng Ma, Peng Yu

llms

arXiv AI

Jun 29

Benchmarking Multi-Modal Graph-based Social Media Popularity Prediction

arXiv:2606. 27539v1 Announce Type: cross Abstract: Social media popularity prediction aims to forecast the future reach or influence of online content from early-stage observations.

By Utkarsh Sahu, Zhisheng Qi, Li Zhu, Yizhao Yang, Jun Li, Ryan Rossi, Yu Wang

llms agents multimodal benchmarks

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