Collaborative Filtering Algorithm Combining Item Category and Dynamic Time Weighting
Wei Su-yu · Jisuanji gongcheng · 2014
When searching the nearest neighbor set,the traditional item-based collaborative filtering algorithm only takes into account the similarity between the items,which ignores the impact of the items categories similarity and the time factor on recommendation.Aiming at the above problems,an improved item-based collaborative filtering algorithm combining items categories similarity and dynamic time weight is proposed.In this algorithm,the items category similarity is introduced to improve the accuracy of the similarity between items,and two kinds of weighting functions are constructed to incorporate temporal information into the prediction algorithm so as to adapt to changes in both user and item characteristics over time.Experimental results show that the proposed algorithm can efficiently trace the drifting of users' interests and items' popularity and improve the accuracy of the prediction.