39 citations · 72 across the 8 of their papers we have counts for
7 papers · 1 filter
Do Generative Recommenders Deepen the Information Cocoon? A Closed-Loop Simulation with LLM-powered User Simulators
Jiyuan Yang, Gengxin Sun, Mengqi Zhang +5
Recommender systems alleviate information overload, yet repeated feedback between recommendations and user interactions can reinforce existing preferences and narrow users' exposur…
Improving Sequential Recommenders through Counterfactual Augmentation of System Exposure
Ziqi Zhao, Zhaochun Ren, Jiyuan Yang +7
In sequential recommendation (SR), system exposure refers to items that are exposed to the user. Typically, only a few of the exposed items would be interacted with by the user. Al…
Constrained Auto-Regressive Decoding Constrains Generative Retrieval
Shiguang Wu, Zhaochun Ren, Xin Xin +5
Generative retrieval seeks to replace traditional search index data structures with a single large-scale neural network, offering the potential for improved efficiency and seamless…
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…
Content-Based Collaborative Generation for Recommender Systems
Yidan Wang, Zhaochun Ren, Weiwei Sun +9
Generative models have emerged as a promising utility to enhance recommender systems. It is essential to model both item content and user-item collaborative interactions in a unifi…
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure
Jiyuan Yang, Yue Ding, Yidan Wang +7
Sequential recommendation (SR) models are typically trained on user-item interactions which are affected by the system exposure bias, leading to the user preference learned from th…
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…