32 citations · 74 across the 18 of their papers we have counts for
6 papers · 1 filter
Token-Controlled Re-ranking for Sequential Recommendation via LLMs
Wenxi Dai, Wujiang Xu, Pinhuan Wang +1
The widespread adoption of Large Language Models (LLMs) as re-rankers is shifting recommender systems towards a user-centric paradigm. However, a significant gap remains: current r…
iVAE: Interest Information Augmentation with Variational Regularizers for Cross-Domain Sequential Recommendation
Xuying Ning, Wujiang Xu, Tianxin Wei +1
Cross-Domain Sequential Recommendation (CDSR) leverages user behaviors across multiple domains to mitigate data sparsity and cold-start challenges in Single-Domain Sequential Recom…
SLMRec: Distilling Large Language Models into Small for Sequential Recommendation
Wujiang Xu, Qitian Wu, Zujie Liang +5
Sequential Recommendation (SR) task involves predicting the next item a user is likely to interact with, given their past interactions. The SR models examine the sequence of a user…
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach
Wujiang Xu, Xuying Ning, Wenfang Lin +7
Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems. The existing approaches a…
Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
Wujiang Xu, Qitian Wu, Runzhong Wang +5
Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity and cold-start problems present in Single-Domain Sequential Recommendation (SDSR). Existing CD…
Neural Node Matching for Multi-Target Cross Domain Recommendation
Wujiang Xu, Shaoshuai Li, Mingming Ha +5
Multi-Target Cross Domain Recommendation(CDR) has attracted a surge of interest recently, which intends to improve the recommendation performance in multiple domains (or systems) s…