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Found 5 papers, 4 papers with code
Our Model Achieves Excellent Performance on MovieLens: What Does it Mean?
First, there are significant differences in user interactions at the different stages when a user interacts with the MovieLens platform.
Retraining A Graph-based Recommender with Interests Disentanglement
Then we blend the historical and new preferences in the form of node embeddings in the new graph, through a Disentanglement Module.
Do Loyal Users Enjoy Better Recommendations? Understanding Recommender Accuracy from a Time Perspective
Our study offers a different perspective to understand recommender accuracy, and our findings could trigger a revisit of recommender model design.
A Critical Study on Data Leakage in Recommender System Offline Evaluation
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.
A Re-visit of the Popularity Baseline in Recommender Systems
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.
