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.
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Acknowledgments
We thank the anonymous reviewers for their valuable comments and suggestions. This work is supported by the Natural Science Foundation of China under Grant No.: 61672311 and 61532011.
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Chen, C., Zhang, M., Liu, Y., Ma, S. (2018). Missing Data Modeling with User Activity and Item Popularity in Recommendation. In: Tseng, YH., et al. Information Retrieval Technology. AIRS 2018. Lecture Notes in Computer Science(), vol 11292. Springer, Cham. https://doi.org/10.1007/978-3-030-03520-4_11
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DOI: https://doi.org/10.1007/978-3-030-03520-4_11
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