基于隐性反馈分析的个性化推荐研究

Research on individualized recommendation based on implicit feedback analyses

针对目前典型电子商务推荐系统中存在的问题,提出了一种基于隐性反馈的信息分析处理方法,详细阐述了系统收集和处理用户个性化信息进行模糊语意分析,从而建立用户兴趣模型的过程。对实验数据的分析表明,该模型实现了推荐系统对用户兴趣的较准确判断,同时能及时有效地掌握用户兴趣偏移,从而改善用户体验,增加用户黏性,进而提高商务网站交易量。

Aimed at the main challenges of E-Commerce recommendation systems that still remained in those systems at present, an individualized adaptive module of user＇ s interest prediction is presented based on implicit feedback analyzation, and thoroughly prescribed the procedure of user interests module foundation by efficiently gathering and processing user＇ s individual information fuzzy linguistic analyses. The experiments show that the E-Commerce site＇ s accurate recommendation services can precisely predict consumer＇ s preference which make recommendation systems ablet to quick cycaptureuser＇s excursion of interests, attract latent users, increaseeonsumer＇ s adhesiveness and enhance site＇ s sales.