【摘要】 结合社会网络分析的推荐方法研究已成为热点。电子商务中用户的动态行为异常丰富,隐含了用户的关联关系,利用这些信息进行商品推荐是个新研究思路。分析电子商务系统中用户动态行为关联关系及用户间明确好友关系形成复杂隐性社会网络,将社团划分算法应用到该网络中,则社团内部用户联系紧密且具有更相似的消费偏好,据此设计了电子商务中社团内部的推荐方法,应用R语言进行了算法的验证并与传统的协同过滤算法进行比较。实验表明,该推荐算法提高了推荐的质量,缓解了传统推荐算法中数据稀疏性及冷启动问题等。更多还原

【Abstract】 Recommended method combined with social network analysis has become a hot spot. The dynamic behavior of users is unusually rich in e- commerce implied the user’s relationship and with the use of the information for recommendation is a new research idea. According to this can construct a complex recessive social network by the user dynamic behavior relationship and clear relationship between users of the e- commerce and using community partition algorithm on it,the internal users are linked closely and have more similar consumption preference,and design a recommended method based on community partition. Using R language for the validation of the proposed algorithm and comparison with the traditional collaborative filtering algorithm. Experiments show that the recommendation algorithm improves the quality of the recommendation and alleviates the data sparseness and cold start problem in traditional recommendation algorithm.更多还原