Normalizing Item-Based Collaborative Filter Using Context-Aware Scaled Baseline Predictor. (5th April 2017)

• Record Type: Journal Article
• Authors: Ma, Wenming; Shi, Junfeng; Zhao, Ruidong
• Other Names: Dumitrescu Bogdan Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2017(2017)
• Publication Date: 2017-04-05
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2017/6562371
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Item-based collaborative filter algorithms play an important role in modern commercial recommendation systems (RSs). To improve the recommendation performance, normalization is always used as a basic component for the predictor models. Among a lot of normalizing methods, subtracting the baseline predictor (BLP) is the most popular one. However, the BLP uses a statistical constant without considering the context. We found that slightly scaling the different components of the BLP separately could dramatically improve the performance. This paper proposed some normalization methods based on the scaled baseline predictors according to different context information. The experimental results show that using context-aware scaled baseline predictor for normalization indeed gets better recommendation performance, including RMSE, MAE, precision, recall, and nDCG.