Design And Implementation Of E-commerce Personalized Recommendation System

With stable growing in the popularity of online shopping, the contradiction between personalpurchase behaving and the huge commodity demand that the website provides is more and more prominent.The phenomenon of "information overload" and "information lost" is growing much more serious. Theapplication of personalized recommendation technology on e-commerce website is becoming increasinglypopular, but with the improvement of people’s shopping requirements, the challenge it faces is becomingmore and more rigorous.First: cold start problem, because new users can take advantage of little behavioral information,it is difficult to give precise recommendation; On the other hand the new products have a small number ofchance to be selected, it is difficult to find a suitable method to give right recommendation.Second: the mining and utilization of user behavior pattern, digging user behavior patterns canimprove the effect of the recommendation to some extent, but the user’s buying behavior will change withthe time, and how to accurately grasp the behavior patterns and making full use of information is a startingpoint for the success of the recommendation system.Third: The selection of personalized recommendation algorithm, personalized recommendationalgorithm including content-based recommendation and collaborative filtering recommendation, and howto select the appropriate recommendation algorithm according to the website directly determines therecommended results.In summary, this paper adopts the hot list and label system to solve the cold start problem, for theuser reaching the product page through search engine, its recommendation is based on the solr full-text search engine and the search keywords related to the products. Mining user behavior pattern by mixedmining way, and implicit and explicit information to determine the user preferences, and the construct ofuser behavior is based on the vector space model. To calculate the keyword weight of product features,thispaper use HTTPCWS system of Chinese word segmentation. Under the analysis of the advantages anddisadvantages of each recommendation algorithm, combining with the actual data,this article presentscombined weighted algorithm to achieve personalized recommendation; In the end this paper realize apersonalized product recommendation system based on the platform of People Mall e-commerce.