110 citations · 264 across the 35 of their papers we have counts for
10 papers · 1 filter
Reward Guided Decoding for Generative Recommendation
Ruochen Yang, Yusheng Huang, Youfeng Zheng +11
Generative recommendation formulates recommendation task into an SID sequence autoregressive generation paradigm, but the decoding process is often dominated by generation likeliho…
Unifying Generative Recall and Multi-Objective Ranking in a Single Decoder-Only Sequence
Ruochen Yang, Shuang Wen, Pengbo Xu +6
Modern industrial recommendation systems typically separate recall and ranking into two independent stages. Although this cascade supports corpus-level retrieval and fine-grained m…
Personalized Multi-Interest Modeling for Cross-Domain Recommendation to Cold-Start Users
Xiaodong Li, Jiawei Sheng, Jiangxia Cao +6
Cross-domain recommendation (CDR) has demonstrated to be an effective solution for alleviating the user cold-start issue. By leveraging rich user-item interactions available in a r…
S2CDR: Smoothing-Sharpening Process Model for Cross-Domain Recommendation
Xiaodong Li, Juwei Yue, Xinghua Zhang +5
User cold-start problem is a long-standing challenge in recommendation systems. Fortunately, cross-domain recommendation (CDR) has emerged as a highly effective remedy for the user…
From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation
Ruochen Yang, Xiaodong Li, Jiawei Sheng +6
Multi-behavior sequential recommendation (MBSR) aims to learn the dynamic and heterogeneous interactions of users' multi-behavior sequences, so as to capture user preferences under…
FARM: Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation
Xiaodong Li, Ruochen Yang, Shuang Wen +10
Live-streaming services have attracted widespread popularity due to their real-time interactivity and entertainment value. Users can engage with live-streaming authors by participa…
Exploring Preference-Guided Diffusion Model for Cross-Domain Recommendation
Xiaodong Li, Hengzhu Tang, Jiawei Sheng +5
Cross-domain recommendation (CDR) has been proven as a promising way to alleviate the cold-start issue, in which the most critical problem is how to draw an informative user repres…
CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process
Xiaodong Li, Jiawei Sheng, Jiangxia Cao +3
Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by tra…
Contrastive Cross-Domain Sequential Recommendation
Jiangxia Cao, Xin Cong, Jiawei Sheng +2
Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challen…
Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck
Jiangxia Cao, Jiawei Sheng, Xin Cong +2
Recommender systems have been widely deployed in many real-world applications, but usually suffer from the long-standing user cold-start problem. As a promising way, Cross-Domain R…