1 citations · 1 across the 5 of their papers we have counts for
8 papers
DiffCold: A Diffusion-based Generative Model for Cold-Start Item Recommendation
Kangning Zhang, Yingjie Qin, Weinan Zhang +2
Cold-start item recommendation remains a persistent challenge in real-world systems due to the absence of interaction histories. While prior models attempt to bridge this gap using…
LASER: An Efficient Target-Aware Segmented Attention Framework for End-to-End Long Sequence Modeling
Tianhe Lin, Ziwei Xiong, Baoyuan Ou +8
Modeling ultra-long user behavior sequences is pivotal for capturing evolving and lifelong interests in modern recommendation systems. However, deploying such models in real-time i…
GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation
Wei Xu, Haoran Li, Baoyuan Ou +4
Cross-domain Click-Through Rate prediction aims to tackle the data sparsity and the cold start problems in online advertising systems by transferring knowledge from source domains…
HoPE: Hybrid of Position Embedding for Long Context Vision-Language Models
Haoran Li, Yingjie Qin, Baoyuan Ou +2
Vision-Language Models (VLMs) have made significant progress in multimodal tasks. However, their performance often deteriorates in long-context scenarios, particularly long videos.…
Diffusion Model for Interest Refinement in Multi-Interest Recommendation
Yankun Le, Haoran Li, Baoyuan Ou +4
Multi-interest candidate matching plays a pivotal role in personalized recommender systems, as it captures diverse user interests from their historical behaviors. Most existing met…
MOTOR: Learning ID-free Item Representation with Token Crossing for Embedding-based Multimodal Recommendation
Kangning Zhang, Jiarui Jin, Yingjie Qin +4
While multimodal recommendation models have effectively integrated visual and textual information, their reliance on unique ID embeddings constitutes a fundamental performance bott…
DREAM: A Dual Representation Learning Model for Multimodal Recommendation
Kangning Zhang, Yingjie Qin, Jiarui Jin +4
Multimodal recommendation focuses primarily on effectively exploiting both behavioral and multimodal information for the recommendation task. However, most existing models suffer f…
AlignRec: Aligning and Training in Multimodal Recommendations
Yifan Liu, Kangning Zhang, Xiangyuan Ren +7
With the development of multimedia systems, multimodal recommendations are playing an essential role, as they can leverage rich contexts beyond interactions. Existing methods mainl…