Improving Top-N Recommendation Performance Using Missing Data

文献类型： 外文期刊

第一作者： Zhao, Xiangyu

作者： Zhao, Xiangyu;Wang, Kaiyi;Liu, Zhongqiang;Zhao, Xiangyu;Niu, Zhendong;Niu, Ke

作者机构：

期刊名称：MATHEMATICAL PROBLEMS IN ENGINEERING （ 影响因子：1.305； 五年影响因子：1.27 ）

ISSN： 1024-123X

年卷期： 2015 年

页码：

收录情况： SCI

摘要： 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.

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