一种融合用户偏好和社交活跃度的推荐算法

为有效解决传统推荐算法精度低的问题,提出了一种融合用户偏好和社交活跃度的概率矩阵分解推荐算法(Probabilistic Matrix Factorization Recommendation Algorithm Combining User Prefer-ence and Social Activity,UPSA-P...

A recommendation algorithm combining user preference and social activity

To effectively solve the problem of poor accuracy of traditional recommendation algorithm,a probabilistic matrix factorization recommendation algorithm combining user preference and social activity(UPSA-PMF) is proposed.When calculating the preference trust between users through user rating data,the common item balance factor and the popular item penalty factor are used for improvement.When calculating the trust in social networks,the relationship between social activity and user trust is considered,and social activity is used as a penalty factor to modify user trust.The preference trust degree and the trust degree in the social network are dynamically combined to obtain the final trust degree.The final degree of trust is combined with the probability matrix model to realize the recommendation.Experiments show that the improved algorithms are better than the existing recommendation algorithms,which effectively improves the quality of recommendations.