基于改进的协同过滤的电子商务网站推荐系统

Recommendation System for E-Commerce Websites Based on Improved Collaborative Filtering

随着互联网的发展以及普及,电子商务网站的访问量与数量庞大,但是发现电子商务网站对用户检索意愿的考虑较少。针对此问题,本文使用一种基于增量改进的协同过滤(CF)的推荐算法(ICFR),首先,通过CF算法来获取用户偏好与所推荐商品和电子商务网站之间的关系;其次,通过分析网络日志来获取用户的浏览信息,并将其归一化作为评分值;最后,通过所设计的增量算法完成历史用户偏好数据信息的更新。我们通过一些基于ICFR模型案例说明ICFR模型适用于电子商务网站的推荐。

With the development and popularization of the Internet, the number of visits to personalized e-commerce website is huge. However, it was found that e-commerce websites gave less consideration to users’ search intentions. To solve this problem, this paper uses an incremental Improved Collaborative Filtering (CF) Recommendation Algorithm (ICFR), firstly, the CF algorithm is used to obtain the relationship between user preferences and recommended products and e-commerce websites. Secondly, the user’s browsing information was obtained by analyzing the network logs, and it was normalized as the scoring value. Finally, the designed incremental algorithm is used to update the historical user preference data information. We illustrate the application of the ICFR model to personalized e-commerce website recommendations through some examples based on the ICFR model.