An optimized item-based collaborative filtering recommendation algorithm based on item genre prediction

De-Jia Zhang

doi:10.1117/12.836808

10 July 2009 An optimized item-based collaborative filtering recommendation algorithm based on item genre prediction

De-Jia Zhang

Proceedings Volume 7490, PIAGENG 2009: Intelligent Information, Control, and Communication Technology for Agricultural Engineering; 74901I (2009) https://doi.org/10.1117/12.836808
Event: International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2009), 2009, Zhangjiajie, China

Abstract

With the fast development of Internet, many systems have emerged in e-commerce applications to support the product recommendation. Collaborative filtering is one of the most promising techniques in recommender systems, providing personalized recommendations to users based on their previously expressed preferences in the form of ratings and those of other similar users. In practice, with the adding of user and item scales, user-item ratings are becoming extremely sparsity and recommender systems utilizing traditional collaborative filtering are facing serious challenges. To address the issue, this paper presents an approach to compute item genre similarity, through mapping each item with a corresponding descriptive genre, and computing similarity between genres as similarity, then make basic predictions according to those similarities to lower sparsity of the user-item ratings. After that, item-based collaborative filtering steps are taken to generate predictions. Compared with previous methods, the presented collaborative filtering employs the item genre similarity can alleviate the sparsity issue in the recommender systems, and can improve accuracy of recommendation.

Citation Download Citation

De-Jia Zhang "An optimized item-based collaborative filtering recommendation algorithm based on item genre prediction", Proc. SPIE 7490, PIAGENG 2009: Intelligent Information, Control, and Communication Technology for Agricultural Engineering, 74901I (10 July 2009); https://doi.org/10.1117/12.836808

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