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http://dx.doi.org/10.6109/jkiice.2019.23.5.495

Missing Data Modeling based on Matrix Factorization of Implicit Feedback Dataset  

Ji, JiaQi (Department of Information Center, Hebei Normal University for Nationalities)
Chung, Yeongjee (Department of Computer and Software Engineering, Wonkwang University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.5, 2019 , pp. 495-507 More about this Journal
Abstract
Data sparsity is one of the main challenges for the recommender system. The recommender system contains massive data in which only a small part is the observed data and the others are missing data. Most studies assume that missing data is randomly missing from the dataset. Therefore, they only use observed data to train recommendation model, then recommend items to users. In actual case, however, missing data do not lost randomly. In our research, treat these missing data as negative examples of users' interest. Three sample methods are seamlessly integrated into SVD++ algorithm and then propose SVD++_W, SVD++_R and SVD++_KNN algorithm. Experimental results show that proposed sample methods effectively improve the precision in Top-N recommendation over the baseline algorithms. Among the three improved algorithms, SVD++_KNN has the best performance, which shows that the KNN sample method is a more effective way to extract the negative examples of the users' interest.
Keywords
Recommender System; Matrix Factorization; Missing Data; Data Sparsity;
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