arXiv AI
Jul 24
PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails
arXiv:2607. 20482v1 Announce Type: new Abstract: Recent advances in large language models have enabled web agents to autonomously execute complex tasks.
Behavioral Fingerprinting and Navigation Prediction in Web Browsing
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.
Related stories
arXiv AI
6d ago
BehaveRec: When Does Long-Term Behavioral History Help Sequential Recommendation? A Study with Session, History, and Community Signals
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.
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.
arXiv Machine Learning
Sep 3
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 Machine Learning
Aug 4
Secrets Everywhere: Auditing Memorization in Mobility Prediction Models
arXiv:2608. 02052v1 Announce Type: new Abstract: Human mobility prediction models, which forecast the next location in a user's trajectory, are increasingly deployed in urban analytics, navigation, and personalized services.
arXiv Machine Learning
Jul 31
Building a User Foundation Model for the Open Web
arXiv:2607. 28019v1 Announce Type: new Abstract: User foundation models have demonstrated strong results in e-commerce and social recommendation, but most industrial deployments assume environments where user identity is stable and persistent.
arXiv AI
Oct 1
SimTrace: Grounded Multimodal User Trajectories Generation for Online User Modeling
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....
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.
arXiv Computation and Language
Oct 27
BehaviorMem: Building and Evaluating User Memory from Real-World E-Commerce Behavior Streams
arXiv:2610.34790v1 Announce Type: new Abstract: User memory helps personalized systems build on their understanding of users over time. In e-commerce, users convey their preferences and needs through...
arXiv Computer Vision
Oct 27
StreamVibe: Proactive Video Agents for Evolving User Intent
arXiv:2610.34662v1 Announce Type: new Abstract: Vision-language models are shifting from offline passive understanding toward online proactive interaction. However, prevailing methods largely rely on...
arXiv Machine Learning
Sep 30
AdaptArena: Evaluating Test-Time Personalization of Web Agents
arXiv:2609.36488v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong performance on complex web navigation tasks, yet they remain brittle in real-world settings...
arXiv AI
Oct 21
HABIT-Bench: Evaluating Habit Induction from Longitudinal Weak Evidence in Agent Memory
arXiv:2610.24520v2 Announce Type: replace Abstract: In long-term interactions with large language model (LLM) agents, users reveal habits through their choices and actions. These habits capture impli...