Personalized Recommendation Algorithm Based on Tags and Collaborative Filtering

doi:10.3969/j.issn.1006-2475.2016.02.014

Abstract

Abstract: Existing collaborative filtering algorithm is primarily based on user’s ratings for resources, but usually user ratings matrix data are sparse, a small amount of data can not express very well the characteristics of users and resources. However, the tag can reflect the user’s interest and can describe the features of the resource. Therefore, by introducing tag, a personalized recommendation algorithm based on tags and collaborative filtering is proposed in this paper. The basic idea is to consider the tag as an intermediate link between the user and resource. Then the correlation degree between user and tag, tag and resource is calculated through splitting three-dimensional relationship among the users, tags and resources, and then build the user’s interest model. Finally, according to the user’s interest model to predict preference values for other new resources, and produce the Top-N recommendation set. Compared with the existing algorithm, the precision and the recall rate of the algorithm proposed are all improved, and reach a better recommended effect.

Key words: tags, collaborative filtering, interest model, personalized recommendation

LIU Jian, ZHANG Kun, CHEN Xuan. Personalized Recommendation Algorithm Based on Tags and Collaborative Filtering[J]. Computer and Modernization, 2016, 0(2): 62-65,71.

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