Recommendation system improves personalization using multi-view temporal thinking
Think Globally, Generate Personally: Multi-View Thinking with Adaptive Generation for Generative Recommendation
Artificial Intelligence
Summary
Recommendation systems try to guess what you might like next, but they often struggle to use all your past actions properly. The authors found that looking at both your full history and your recent activities separately and then combining them helps make better guesses. They also designed a way to tailor the recommendation process to each user more precisely. Their approach, called MV-Thinker, uses these ideas to pick items more accurately across multiple tests.
What this means in practice
- •For e-commerce developers: Enhance product suggestions by combining users’ full and recent shopping histories for more relevant item recommendations.
- •For streaming service engineers: Improve personalized content recommendations by adaptively adjusting generated suggestions based on evolving user preferences.
Authors
Yuxuan Hu, Yingyi Zhang, Wenlin Zhang, Chao Zhang, Haotian Nan, Ziwei Liu, Jiancheng Dong, Tongzhou Wu, Beibei Kong, Lei Cheng, Chengxiang Zhuo, Zang Li, Wanyu Wang, Xiangyu Zhao
Abstract
Semantic-ID-based (SID) generative recommendation (GR) represents each item as a sequence of discrete tokens and predicts the next item through autoregressive SID generation. However, existing methods face two challenges. First, insufficient shared context modeling for user sequences: full-history and recent behaviors provide overlapping yet complementary evidence, but modeling them within a single sequence may dilute current cues, whereas processing them through separate streams may fail to coordinate them. Second, insufficient personalized adaptation in progressive SID decisions: although decoder states evolve with generated prefixes, existing models typically use a shared output-side transformation across recommendation instances, without explicitly selecting a transformation suited to the current user need. To address these challenges, we propose Multi-View Thinking with Adaptive Generation (MV-Thinker), a framework that Thinks Globally and Generates Personally. First, Multi-View Temporal Thinking uses view-specific thinking queries and readers over overlapping full-history and recent-history views, coordinating both sources into complementary user-conditioned contexts before SID generation. Second, Hierarchical Residual Adaptation organizes lightweight adapters into nested residual paths before a shared vocabulary projection, adaptively transforming context-bearing decoder states for SID decisions. Extensive experiments on four public datasets demonstrate that MV-Thinker consistently outperforms strong discriminative and generative recommendation baselines across multiple ranking metrics. The implementation code and datasets are available on: https://anonymous.4open.science/r/MV-Thinker