20 citations · 66 across the 26 of their papers we have counts for
13 papers · 1 filter
Breaking the Top- Barrier: Advancing Top- Ranking Metrics Optimization in Recommender Systems
Weiqin Yang, Jiawei Chen, Shengjia Zhang +5
In the realm of recommender systems (RS), Top- ranking metrics such as NDCG@ are the gold standard for evaluating recommendation performance. However, during the training of…
Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution
Shengjia Zhang, Jiawei Chen, Changdong Li +5
Loss functions play a pivotal role in optimizing recommendation models. Among various loss functions, Softmax Loss (SL) and Cosine Contrastive Loss (CCL) are particularly effective…
Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System
Chao Wang, Yue Zheng, Yujing Zhang +5
In a single-slot recommendation system, users are only exposed to one item at a time, and the system cannot collect user feedback on multiple items simultaneously. Therefore, only…
MSL: Not All Tokens Are What You Need for Tuning LLM as a Recommender
Bohao Wang, Feng Liu, Jiawei Chen +7
Large language models (LLMs), known for their comprehension capabilities and extensive knowledge, have been increasingly applied to recommendation systems (RS). Given the fundament…
Rankformer: A Graph Transformer for Recommendation based on Ranking Objective
Sirui Chen, Shen Han, Jiawei Chen +6
Recommender Systems (RS) aim to generate personalized ranked lists for each user and are evaluated using ranking metrics. Although personalized ranking is a fundamental aspect of R…
LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation
Bohao Wang, Feng Liu, Changwang Zhang +8
Sequential Recommenders generate recommendations based on users' historical interaction sequences. However, in practice, these collected sequences are often contaminated by noisy i…
How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
Siyi Lin, Chongming Gao, Jiawei Chen +5
Recommendation Systems (RS) are often plagued by popularity bias. When training a recommendation model on a typically long-tailed dataset, the model tends to not only inherit this…
Distributionally Robust Graph-based Recommendation System
Bohao Wang, Jiawei Chen, Changdong Li +6
With the capacity to capture high-order collaborative signals, Graph Neural Networks (GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their efficacy of…
CDR: Conservative Doubly Robust Learning for Debiased Recommendation
ZiJie Song, JiaWei Chen, Sheng Zhou +4
In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data. Consequently, tackling bias has emerged as a…
GReS: Graphical Cross-domain Recommendation for Supply Chain Platform
Zhiwen Jing, Ziliang Zhao, Yang Feng +6
Supply Chain Platforms (SCPs) provide downstream industries with numerous raw materials. Compared with traditional e-commerce platforms, data in SCPs is more sparse due to limited…
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
Jiawei Chen, Chengquan Jiang, Can Wang +5
Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data. A typical strategy is to draw negative instanc…
SamWalker++: recommendation with informative sampling strategy
Can Wang, Jiawei Chen, Sheng Zhou +3
Recommendation from implicit feedback is a highly challenging task due to the lack of reliable negative feedback data. Existing methods address this challenge by treating all the u…
Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback
Jiawei Chen, Can Wang, Sheng Zhou +4
Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendat…