摘要
推荐系统是互联网应用中的关键技术之一,该系统通过分析用户行为,用主动向用户推荐产品的方式替代被动地接受用户请求。优秀的推荐系统不仅可以提高用户体验,还能增加用户购买欲望。协同过滤算法是推荐系统中广泛应用的算法之一。在大规模网络中,传统协同过滤算法将出现极端稀疏问题,且算法效率低下。设计了一种通过对网络分割、分组的协同过滤算法,该算法的目的是将大规模网络通过一定的分割规则分割并分组,利用分治的思想,将问题分解为子问题然后求解,以优化算法性能。
Recommender system is one of the key techniques in internet applications.The system analyzes user＇s behavior,and recommends products initiative to replace the passive acceptance of user requests.The recommender system can improve not only the user experience but also the user＇s desire to buy something.Collaborative filtering algorithm is widely used in the recommender system.In a largescale network,traditional collaborative filtering algorithms have extreme sparseness problem,and thus being inefficient.A collaborative filtering algorithm is proposed,which is designed by the large-scale network segmentation rules.The algorithm uses the idea of divide and conquer algorithm,and decomposes problems into sub-problems to solve,hence reaching the optimization of algorithm.
出处
《计算机工程与科学》
CSCD
北大核心
2013年第12期107-113,共7页
Computer Engineering & Science
基金
国家自然科学基金资助项目(61272543)
关键词
推荐系统
相似度计算
协同过滤
网络分割
recommender system
similarity calculation
collaborative filtering
network segmentation
