39 citations · 69 across the 7 of their papers we have counts for
7 papers
Beyond Fixed Depths and Widths: Optimizing Textual Decoding Tries in LLM-based Generative Recommendation
Jingzhe Liu, Hanbing Wang, Jiliang Tang +4
Generative recommendation (GR) is an increasingly popular paradigm in recommender systems, with a prominent line of work using LLMs as autoregressive backbones to predict the next…
An Embarrassingly Simple Graph Heuristic Reveals Shortcut-Solvable Benchmarks for Sequential Recommendation
Haoyu Han, Li Ma, Hanbing Wang +9
Sequential recommendation has increasingly shifted toward generative recommenders that combine sequential patterns with semantic item information. Yet these methods are often evalu…
SA-CAISR: Stage-Adaptive and Conflict-Aware Incremental Sequential Recommendation
Xiaomeng Song, Xinru Wang, Hanbing Wang +4
Sequential recommendation (SR) aims to predict a user's next action by learning from their historical interaction sequences. In real-world applications, these models require period…
Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation
Jiyuan Yang, Yuanzi Li, Jingyu Zhao +8
Sequential Recommenders have been widely applied in various online services, aiming to model users' dynamic interests from their sequential interactions. With users increasingly en…
Rethinking Large Language Model Architectures for Sequential Recommendations
Hanbing Wang, Xiaorui Liu, Wenqi Fan +7
Recently, sequential recommendation has been adapted to the LLM paradigm to enjoy the power of LLMs. LLM-based methods usually formulate recommendation information into natural lan…
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
Xin Xin, Xiangyuan Liu, Hanbing Wang +8
Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target…
On the User Behavior Leakage from Recommender System Exposure
Xin Xin, Jiyuan Yang, Hanbing Wang +6
Modern recommender systems are trained to predict users potential future interactions from users historical behavior data. During the interaction process, despite the data coming f…