关键词: 协同过滤推荐算法, CFDP算法, 时间因子, 平均绝对偏差

Abstract:

In this era of exponential explosion of network data, how to use user behavior data to accurately recommend each project target is a valuable research direction. Collaborative filtering recommendation algorithm is one of the most classic recommendation algorithms. How to optimize the traditional collaborative filtering algorithm is the research content of this paper. Data sparse, cold start and real-time problems exist for traditional collaborative filtering algorithms. In this paper, the CFDP algorithm is used to cluster the project collection, and the Slope-One algorithm is used to fill the data, which effectively alleviates the problem of data sparseness and cold start. For the real-time problem of the recommendation system, this paper introduces the time factor. Each prediction score is multiplied by the time weight, making the prediction score more scientific and accurate, and solving the real-time problem of the recommendation system. The traditional collaborative filtering algorithm and the improved collaborative filtering algorithm are compared by MovieLens 1M dataset. The average absolute deviation MAE of the new algorithm is smaller than the traditional collaborative filtering recommendation algorithm, which indicates that the improved algorithm effectively optimizes the traditional algorithm.

Key words: collaborative filtering recommendation algorithm, CFDP algorithm, time factor, mean absolute deviation