1 code implementation • 19 Jul 2023 • Yu-chen Fan, Yitong Ji, Jie Zhang, Aixin Sun
First, there are significant differences in user interactions at the different stages when a user interacts with the MovieLens platform.
no code implementations • 5 May 2023 • Yitong Ji, Aixin Sun, Jie Zhang
Then we blend the historical and new preferences in the form of node embeddings in the new graph, through a Disentanglement Module.
1 code implementation • 12 Apr 2022 • Yitong Ji, Aixin Sun, Jie Zhang, Chenliang Li
Our study offers a different perspective to understand recommender accuracy, and our findings could trigger a revisit of recommender model design.
1 code implementation • 21 Oct 2020 • Yitong Ji, Aixin Sun, Jie Zhang, Chenliang Li
To evaluate recommendation systems in a realistic manner in offline setting, we propose a timeline scheme, which calls for a revisit of the recommendation model design.
1 code implementation • 28 May 2020 • Yitong Ji, Aixin Sun, Jie Zhang, Chenliang Li
On the widely used MovieLens dataset, we show that the performance of popularity could be significantly improved by 70% or more, if we consider the popular items at the time point when a user interacts with the system.