16 citations · 32 across the 11 of their papers we have counts for
7 papers · 1 filter
Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach
Jialei Chen, Yuanbo Xu, Yiheng Jiang
In this paper, we focus on the often-overlooked issue of embedding collapse in existing diffusion-based sequential recommendation models and propose ADRec, an innovative framework…
DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops
Hangtong Xu, Yuanbo Xu, Yongjian Yang +2
Recommendation models trained on the user feedback collected from deployed recommendation systems are commonly biased. User feedback is considerably affected by the exposure mechan…
Causal Structure Representation Learning of Confounders in Latent Space for Recommendation
Hangtong Xu, Yuanbo Xu, Chaozhuo Li +1
Inferring user preferences from the historical feedback of users is a valuable problem in recommender systems. Conventional approaches often rely on the assumption that user prefer…
Separating and Learning Latent Confounders to Enhancing User Preferences Modeling
Hangtong Xu, Yuanbo Xu, Yongjian Yang
Recommender models aim to capture user preferences from historical feedback and then predict user-specific feedback on candidate items. However, the presence of various unmeasured…
Detect Professional Malicious User with Metric Learning in Recommender Systems
Yuanbo Xu, Yongjian Yang, En Wang +2
In e-commerce, online retailers are usually suffering from professional malicious users (PMUs), who utilize negative reviews and low ratings to their consumed products on purpose t…
A Unified Collaborative Representation Learning for Neural-Network based Recommender Systems
Yuanbo Xu, En Wang, Yongjian Yang +1
Most NN-RSs focus on accuracy by building representations from the direct user-item interactions (e.g., user-item rating matrix), while ignoring the underlying relatedness between…
Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems
Yuanbo Xu, Yongjian Yang, En Wang
Classical accuracy-oriented Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predict…