82 citations · 141 across the 5 of their papers we have counts for
6 papers
Hesitation and Tolerance in Recommender Systems
Kuan Zou, Aixin Sun, Yitong Ji +5
Users' interactions with recommender systems often involve more than simple acceptance or rejection. We highlight two overlooked states: hesitation, when people deliberate without…
Our Model Achieves Excellent Performance on MovieLens: What Does it Mean?
Yu-chen Fan, Yitong Ji, Jie Zhang +1
A typical benchmark dataset for recommender system (RecSys) evaluation consists of user-item interactions generated on a platform within a time period. The interaction generation m…
Retraining A Graph-based Recommender with Interests Disentanglement
Yitong Ji, Aixin Sun, Jie Zhang
In a practical recommender system, new interactions are continuously observed. Some interactions are expected, because they largely follow users' long-term preferences. Some other…
Do Loyal Users Enjoy Better Recommendations? Understanding Recommender Accuracy from a Time Perspective
Yitong Ji, Aixin Sun, Jie Zhang +1
In academic research, recommender systems are often evaluated on benchmark datasets, without much consideration about the global timeline. Hence, we are unable to answer questions…
A Critical Study on Data Leakage in Recommender System Offline Evaluation
Yitong Ji, Aixin Sun, Jie Zhang +1
Recommender models are hard to evaluate, particularly under offline setting. In this paper, we provide a comprehensive and critical analysis of the data leakage issue in recommende…
A Re-visit of the Popularity Baseline in Recommender Systems
Yitong Ji, Aixin Sun, Jie Zhang +1
Popularity is often included in experimental evaluation to provide a reference performance for a recommendation task. To understand how popularity baseline is defined and evaluated…