5 citations · 10 across the 6 of their papers we have counts for
7 papers
Multimodal Decoupling for Diversified Recommendation through Semantic and Structural Shaping
Changran He, Jiaming Wei, Jianghong Ma +3
Recommender systems often suffer from over-concentrated results when optimized for accuracy, limiting recommendation diversity. While diversity has become an important objective, e…
Diversity Recommendation via Causal Deconfounding of Co-purchase Relations and Counterfactual Exposure
Jingmao Zhang, Zhiting Zhao, Yunqi Lin +4
Beyond user-item modeling, item-to-item relationships are increasingly used to enhance recommendation. However, common methods largely rely on co-occurrence, making them prone to i…
Dual-Phase Playtime-guided Recommendation: Interest Intensity Exploration and Multimodal Random Walks
Jingmao Zhang, Zhiting Zhao, Yunqi Lin +4
The explosive growth of the video game industry has created an urgent need for recommendation systems that can scale with expanding catalogs and maintain user engagement. While pri…
Multi-Objective Recommendation in the Era of Generative AI: A Survey of Recent Progress and Future Prospects
Zihan Hong, Yushi Wu, Zhiting Zhao +4
With the recent progress in generative artificial intelligence (Generative AI), particularly in the development of large language models, recommendation systems are evolving to bec…
RocketEval: Efficient Automated LLM Evaluation via Grading Checklist
Tianjun Wei, Wei Wen, Ruizhi Qiao +2
Evaluating large language models (LLMs) in diverse and challenging scenarios is essential to align them with human preferences. To mitigate the prohibitive costs associated with hu…
Collaborative Residual Metric Learning
Tianjun Wei, Jianghong Ma, Tommy W. S. Chow
In collaborative filtering, distance metric learning has been applied to matrix factorization techniques with promising results. However, matrix factorization lacks the ability of…
Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation
Tianjun Wei, Jianghong Ma, Tommy W. S. Chow
Collaborative filtering (CF) is widely searched in recommendation with various types of solutions. Recent success of Graph Convolution Networks (GCN) in CF demonstrates the effecti…