Research on personalized recommendation algorithm based on fusion product review evaluation index
Cangying Chen · 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) · 2021
The advent of the Internet era has brought about the rapid development of e-commerce. With the increasing number of online shopping users and online shopping products (services), the problem of information overload has also received more and more attention. Therefore, important tools such as recommendation systems and search engines have emerged, which can better help consumers make decisions among many products and improve purchase efficiency. This article proposes a targeted personalized recommendation algorithm for users who have a clear purchase goal. Combining search engines and keywords entered by users, the product candidate set is filtered out, and then sorted according to the comprehensive score of the product, and recommended to the user to rank top N products, increase the transaction rate.