基于内容过滤的电子商务推荐系统研究

Research on E-Commerce Recommender System Based on Content-Based Filtering

个性化推荐在网络应用中能有效提高服务质量,在电子商务中的表现更加突出。论述了基于内容过滤的电子商务推荐系统,利用向量空间模型挖掘用户独特的兴趣特征,然后根据产品信息特征的量化值产生推荐序列,并根据用户的反馈信息自适应学习,以提高系统的综合性能。实验结果表明,基于内容过滤的推荐方法其总体性能随时间的推移得到了提高。

The application of personalized recommendation in the Internet effectively improved its service, especially the service of E - commerce, content- based filtering E- commerce recommender syste^n was discussed fully in this paper. Users＇ unique features can be explored by means of vector space model （VSM） firstly. Then based on the qualitative value of products information, the recommender lists were obtained. Since the system can adapt to the users＇ feedback automatically, its performance were improved comprehensively. According to the experiments result, the overall performance of the recommender based on content - based filtering was enhanced with time.