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Mathematical Problems in Engineering
Volume 2015, Article ID 380472, 13 pages
Research Article

Improving Top-N Recommendation Performance Using Missing Data

1Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
2School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China

Received 23 April 2015; Accepted 26 August 2015

Academic Editor: Jean-Charles Beugnot

Copyright © 2015 Xiangyu Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recommender systems become increasingly significant in solving the information explosion problem. Data sparse is a main challenge in this area. Massive unrated items constitute missing data with only a few observed ratings. Most studies consider missing data as unknown information and only use observed data to learn models and generate recommendations. However, data are missing not at random. Part of missing data is due to the fact that users choose not to rate them. This part of missing data is negative examples of user preferences. Utilizing this information is expected to leverage the performance of recommendation algorithms. Unfortunately, negative examples are mixed with unlabeled positive examples in missing data, and they are hard to be distinguished. In this paper, we propose three schemes to utilize the negative examples in missing data. The schemes are then adapted with SVD++, which is a state-of-the-art matrix factorization recommendation approach, to generate recommendations. Experimental results on two real datasets show that our proposed approaches gain better top-N performance than the baseline ones on both accuracy and diversity.