Research And Implementation Of Personalized Shopping Recommendation System

With the advent of the era of data technology,customers hope that the E-commerce website can understand their own interests and hobbies.The high-performance requirements of the E-commerce website also make the Recommendation system face more severe challenges.The focus of this paper is:how to improve the recommendation performance of the E-commerce website,improve the existing recommendation technology,dig out the customers' preference characteristics,and discover their potential consumption needs.The work and innovations done in this article are as follows:Firstly,this paper proposes a product recommendation algorithm based on a multi-information probability matrix.The algorithm comprehensively considered the influences of similarly interested neighbor buyers,commodity influence relationships,and behavior sequence on the Product recommendation list.The algorithm combines the above three features into a new model through a Linear Weighting Matrix,and determines the optimal weight coefficient through trial and error,and filter out the recommended list of Top-N commodities according to the score from high to low.Secondly,this paper proposes a recommendation algorithm based on commodity-log analysis.The algorithm can predict the actual transaction rate.In this paper,the top-rated Top-N item recommendation list obtained by the hybrid recommendation method is used as the empirical research background,and the hybrid recommendation method is combined with the consumer clustering algorithm.First,take the consumer's product access log from the browser as the original data mining object,extract the characteristic information of the consumers.Then,the improved K-Means algorithm is applied to the consumer clusters,and consumers with similar interests and hobbies are classified into the same cluster.Finally,use the questionnaire to test the user's satisfaction with the recommended results.The test results prove that the recommendation algorithm proposed in this paper effectively solves the problem of the monotonic user rating element,and the overall user satisfaction is pretty good.Finally,based on the above two recommendation algorithms,a complete E-commerce website is developed and designed.Firstly,the overall framework and technical architecture of the system are introduced.Then,the implementation of the recommendation algorithm proposed in this paper in the website is described in detail,and the final operation effect of the system is shown.Finally,the function test and stress test of the E-commerce website are carried out,Through the analysis of the test results,the effectiveness of the algorithm deployed to the recommendation system is verified.