关键词: 协同过滤, LDA聚类, 时间柱, 概率加权, 主题模型

Abstract: To solve the problem that collaborative filtering algorithm only relies on scoring matrix to generate prediction, which leads to its low recommendation accuracy, a hybrid recommendation algorithm based on time weighted and LDA clustering is proposed. Firstly, the time column model is constructed, time weighted similarity is generated by user rating and scoring time, and the prediction score of time weighted is generated with weighted proportional means. Then the project type is generated by LDA clustering to produce the topic cluster, the prediction score of the LDA clustering is generated by probability transition. Finally, the adjustment factor determines the weight coefficient of two grades, and the final score is generated by linear weighting. Experimental results show that the new algorithm can give reasonable recommendation based on the number of adjacent neighbors and improve the recommendation accuracy.

Key words: collaborative filtering, LDA clustering, time column, probability weighting, topic model