1 citations · 1 across the 5 of their papers we have counts for
6 papers
FITMM: Adaptive Frequency-Aware Multimodal Recommendation via Information-Theoretic Representation Learning
Wei Yang, Rui Zhong, Yiqun Chen +4
Multimodal recommendation aims to enhance user preference modeling by leveraging rich item content such as images and text. Yet dominant systems fuse modalities in the spatial doma…
Reward Balancing Revisited: Enhancing Offline Reinforcement Learning for Recommender Systems
Wenzheng Shu, Yanxiang Zeng, Yongxiang Tang +6
Offline reinforcement learning (RL) has emerged as a prevalent and effective methodology for real-world recommender systems, enabling learning policies from historical data and cap…
Optimal Return-to-Go Guided Decision Transformer for Auto-Bidding in Advertisement
Hao Jiang, Yongxiang Tang, Yanxiang Zeng +5
In the realm of online advertising, advertisers partake in ad auctions to obtain advertising slots, frequently taking advantage of auto-bidding tools provided by demand-side platfo…
Personalized Tree-Based Progressive Regression Model for Watch-Time Prediction in Short Video Recommendation
Xiaokai Chen, Xiao Lin, Changcheng Li +1
In online video platforms, accurate watch time prediction has become a fundamental and challenging problem in video recommendation. Previous research has revealed that the accuracy…
HCMRM: A High-Consistency Multimodal Relevance Model for Search Ads
Guobing Gan, Kaiming Gao, Li Wang +2
Search advertising is essential for merchants to reach the target users on short video platforms. Short video ads aligned with user search intents are displayed through relevance m…
Prompt Tuning for Item Cold-start Recommendation
Yuezihan Jiang, Gaode Chen, Wenhan Zhang +6
The item cold-start problem is crucial for online recommender systems, as the success of the cold-start phase determines whether items can transition into popular ones. Prompt lear…