Missing Data Modeling with User Activity and Item Popularity in Recommendation

Abstract

User feedback such as movie watching history, ratings and consumptions of products, is valuable for improving the performance of recommender systems. However, only a few interactions between users and items can be observed in implicit data. The missing of a user-item entry is caused by two reasons: the user didn’t see the item (in most cases); or the user saw but disliked it. Separating these two cases leads to modeling missing interactions at a finer granularity, which is helpful in understanding users’ preferences more accurately. However, the former case has not been well-studied in previous work. Most existing studies resort to assign a uniform weight to the missing data, while such a uniform assumption is invalid in real-world settings. In this paper, we propose a novel approach to weight the missing data based on user activity and item popularity, which is more effective and flexible than the uniform-weight assumption. Experimental results based on 2 real-world datasets (Movielens, Flixster) show that our approach outperforms 3 state-of-the-art models including BPR, WMF, and ExpoMF.