User fuzzy similarity-based collaborative filtering recommendation algorithm - tao WUYi; ming ZHANGXing; mao WANGXing; Han LI (Tongxin xuebao)
In order to reflect the actual case of human decisions and solve the data sparseness problem of traditional col-laborative filtering recommendation algorithm, a trapezoid fuzzy model based on age fuzzy model was proposed. In this model, crisp point was fuzzified into trapezoid fuzzy mber and the fuzziness and information of users' grade was taken into account when calculating user's similarity by trapezoid fuzzy number. Based on this model, the user fuzzy similari-ty-based collaborative filtering recommendation algorithm was designed. The algorithm was proved to be an extension of traditional collaborative filtering algorithm in fuzzy fields. The experimental results show that, the proposed algorithm performs better when implemented in the sparse dataset with more user than item, and its running time is much less than traditional collaborative filtering algorithm.