Personalized Search Engine Optimization for E-Commerce Platforms Based on Content Filtering Algorithm

With the rise of the mobile Internet era, e-commerce platforms continue to increase, and users have higher and higher requirements for service quality. In order to meet the needs of users, personalized recommendation systems have emerged as an emerging business ecosystem. This article aims to study how to build a personalized information filtering system based on content and structure, and analyze the application of e-commerce companies through actual cases. First, this article introduces content retrieval technology, search engine optimization algorithms, and keyword matching methods. Content retrieval technology is a process of information retrieval by analyzing and matching text content. Search engine optimization algorithms optimize the structure and content of web pages to rank them higher in search engines and improve the effectiveness of information retrieval. The keyword matching method is to match the keywords entered by the user with the keywords of the product or service to achieve more accurate recommendation results. This article tests the performance of the search engine. The test results show that text accounts for 51%, images account for 24%, videos account for 15%, and news accounts for 10%; the matching degree range of text is 94%-100%, the matching degree range of pictures is 80%-87%, the matching degree range of video is 76%-86%, and the matching degree range of news is 75%-82%. This meets the different information needs of users.