A Rating-Based Integrated Recommendation Framework with Improved Collaborative Filtering Approaches

IAENG Internaitonal journal of computer science

Shulin Cheng¹  Bofeng Zhang²  Guobing Zou² 

[1] 1. School of Computer Engineering and Science, Shanghai University99 Shangda Road, BaoShan District, Shanghai, 200444, PR, [e-mail]2. School of Computer and Information, Anqing Normal University1318 Jixian North Road, Anqing, Anhui Province, 246133, PR, [e-mail];School of Computer Engineering and Science, Shanghai University99 Shangda Road, BaoShan District, Shanghai, 200444, PR, China

关键词: personalized recommendation;    collaborative filtering;    rating integration;   

DOI  :  10.15837/ijccc.2017.3.2692

学科分类：计算机科学（综合）

来源: International Association of Engineers

【 摘 要 】

Collaborative filtering (CF) approach is successfully applied in the rating prediction of personal recommendation. But individual information source is leveraged in many of them, i.e., the information derived from single perspective is used in the user-item matrix for recommendation, such as user-based CF method mainly utilizing the information of user view, item-based CF method mainly exploiting the information of item view. In this paper, in order to take full advantage of multiple information sources embedded in user-item rating matrix, we proposed a rating-based integrated recommendation framework of CF approaches to improve the rating prediction accuracy. Firstly, as for the sparsity of the conventional item-based CF method, we improved it by fusing the inner similarity and outer similarity based on the local sparsity factor. Meanwhile, we also proposed the improved user-based CF method in line with the user-item-interest model (UIIM) by preliminary rating. Second, we put forward a background method called user-item-based improved CF (UIBCF-I), which utilizes the information source of both similar items and similar users, to smooth itembased and user-based CF methods. Lastly, we leveraged the three information sources and fused their corresponding ratings into an Integrated CF model (INTE-CF). Experiments demonstrate that the proposed rating-based INTE-CF indeed improves the prediction accuracy and has strong robustness and low sensitivity to sparsity of dataset by comparisons to other mainstream CF approaches.

【 授权许可】

CC BY