基于认知度与兴趣度的好友推荐反馈算法

Friend Recommendation Feedback Algorithm Combining Cognition and Interest

针对现有的好友推荐算法在好友关系刻画上丢失重要信息的现状,受用户对物品认知行为的启发,文中提出基于认知度与兴趣度的好友推荐反馈算法,使用混合相似度研究网络好友关系,探索在线社交网络中的交友问题.针对好友推荐过程中“开环”的问题,提出基于历史推荐信息的正负反馈优化调整策略,使用用户相似度修正公式研究好友反馈动态推荐,证明好友推荐是一个逐步修正的复杂过程,揭示在线社交网络中好友关系刻画的心理学认知问题和推荐的动态变化问题.实验表明,文中算法提高推荐质量,实现用户相似度矩阵的动态调整,在准确率、召回率、鲁棒性、可扩展性等方面性能较优.

In the existing friend recommendation algorithm,important information is lost in the portrayal of the friend relationship.Inspired by the user′s cognitive behavior of the item,a friend recommendation feedback algorithm based on cognition and interest is proposed in this paper.Hybrid similarity is utilized to conduct online friend relationship research and explore friendship issues in online social networks.Aiming at the open loop problem of the friend recommendation process,a positive and negative feedback optimization adjustment strategy based on historical recommendation information is proposed.The user similarity correction formula is employed for friend feedback dynamic recommendation,and it is proved that friend recommendation is a complex process of gradual correction.The psychological and cognitive problems portrayed by friend relationships in online social networks and the dynamic changes of recommendations are presented.The experiments show that the proposed algorithm improves the recommendation quality and realizes the dynamic adjustment of the user similarity matrix and it is superior in accuracy,recall,robustness and scalability.