169 citations · 375 across the 26 of their papers we have counts for
15 papers · 1 filter
Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias
Miaomiao Cai, Lei Chen, Yifan Wang +5
Collaborative Filtering (CF) typically suffers from the significant challenge of popularity bias due to the uneven distribution of items in real-world datasets. This bias leads to…
Large Language Models as Evaluators for Recommendation Explanations
Xiaoyu Zhang, Yishan Li, Jiayin Wang +4
The explainability of recommender systems has attracted significant attention in academia and industry. Many efforts have been made for explainable recommendations, yet evaluating…
ReChorus2.0: A Modular and Task-Flexible Recommendation Library
Jiayu Li, Hanyu Li, Zhiyu He +4
With the applications of recommendation systems rapidly expanding, an increasing number of studies have focused on every aspect of recommender systems with different data inputs, m…
Double Correction Framework for Denoising Recommendation
Zhuangzhuang He, Yifan Wang, Yonghui Yang +6
As its availability and generality in online services, implicit feedback is more commonly used in recommender systems. However, implicit feedback usually presents noisy samples in…
Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty
Peijie Sun, Yifan Wang, Min Zhang +5
With the surge in mobile gaming, accurately predicting user spending on newly downloaded games has become paramount for maximizing revenue. However, the inherently unpredictable na…
A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models
Jiayin Wang, Fengran Mo, Weizhi Ma +3
Large language models (LLMs) are essential tools that users employ across various scenarios, so evaluating their performance and guiding users in selecting the suitable service is…
EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation
Shaorun Zhang, Zhiyu He, Ziyi Ye +4
In recent years, short video platforms have gained widespread popularity, making the quality of video recommendations crucial for retaining users. Existing recommendation systems p…
Aiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation
Hanyu Li, Weizhi Ma, Peijie Sun +6
Cross-domain recommender (CDR) systems aim to enhance the performance of the target domain by utilizing data from other related domains. However, irrelevant information from the so…
Sequential Recommendation with Latent Relations based on Large Language Model
Shenghao Yang, Weizhi Ma, Peijie Sun +4
Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods r…
Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
Shenghao Yang, Weizhi Ma, Peijie Sun +4
Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performan…
A Situation-aware Enhancer for Personalized Recommendation
Jiayu Li, Peijie Sun, Chumeng Jiang +3
When users interact with Recommender Systems (RecSys), current situations, such as time, location, and environment, significantly influence their preferences. Situations serve as t…
Recommender for Its Purpose: Repeat and Exploration in Food Delivery Recommendations
Jiayu Li, Aixin Sun, Weizhi Ma +2
Recommender systems have been widely used for various scenarios, such as e-commerce, news, and music, providing online contents to help and enrich users' daily life. Different scen…
Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering
Peijie Sun, Le Wu, Kun Zhang +2
While effective in recommendation tasks, collaborative filtering (CF) techniques face the challenge of data sparsity. Researchers have begun leveraging contrastive learning to intr…
Intersectional Two-sided Fairness in Recommendation
Yifan Wang, Peijie Sun, Weizhi Ma +4
Fairness of recommender systems (RS) has attracted increasing attention recently. Based on the involved stakeholders, the fairness of RS can be divided into user fairness, item fai…
Understanding User Experience in Large Language Model Interactions
Jiayin Wang, Weizhi Ma, Peijie Sun +2
In the rapidly evolving landscape of large language models (LLMs), most research has primarily viewed them as independent individuals, focusing on assessing their capabilities thro…