关键词: 个体影响力, 推荐系统, 信任传递, 非负矩阵分解

Abstract: According to data sparsity and recommendation accuracy problem in recommender systems, a matrix decomposition algorithm is proposed to preserve the original data structure. Using the projection structure of Non-Negative matrix factorization algorithm, combining the trust propagation model and the personal compact calculation model and user rating prediction model, a fusion of influential individuals projection of the structure of non-negative matrix factorization recommendation algorithm is proposed to improve the precision of the recommendation system. Through the experiments prove that, compared with other algorithms, this algorithm not only reduces the MAE and RMSE, but also improves the prediction accuracy of the system.

Key words: Personal Compact, recommender systems, trust propagation, non-negative matrix factorization