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DTRec: Learning Dynamic Reasoning Trajectories for Sequential Recommendation
Yifan Shao, Peilin Zhou, Shoujin Wang +3
Inspired by advances in LLMs, reasoning-enhanced sequential recommendation performs multi-step deliberation before making final predictions, unlocking greater potential for capturi…
Intent-Guided Reasoning for Sequential Recommendation
Yifan Shao, Peilin Zhou
Sequential recommendation systems aim to capture users' evolving preferences from their interaction histories. Recent reasoningenhanced methods have shown promise by introducing de…
Structural and Disentangled Adaptation of Large Vision Language Models for Multimodal Recommendation
Zhongtao Rao, Peilin Zhou, Dading Chong +3
Multimodal recommendation enhances accuracy by leveraging visual and textual signals, and its success largely depends on learning high-quality cross-modal representations. Recent a…
What Matters in LLM-Based Feature Extractor for Recommender? A Systematic Analysis of Prompts, Models, and Adaptation
Kainan Shi, Peilin Zhou, Ge Wang +2
Using Large Language Models (LLMs) to generate semantic features has been demonstrated as a powerful paradigm for enhancing Sequential Recommender Systems (SRS). This typically inv…
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation
Peilin Zhou, You-Liang Huang, Yueqi Xie +4
Sequential recommender systems (SRS) are designed to predict users' future behaviors based on their historical interaction data. Recent research has increasingly utilized contrasti…