基于组合标签的个性化推荐算法OA

With the rapid development of the Internet and the improvement of people's living standards,personalized customiza-tion has become an important trend of modern consumption.To solve the problems of asymmetry between user needs and product information,poor algorithm recommendation effect and unstable algorithm operation when there is too much or too little user in-teraction data in the personalized customization of existing products,a personalized recommendation algorithm based on combina-tion label is proposed.Based on the designed user portrait and product portrait,the algorithm constructs a combination label(Portrait-Label-Portrait,PLP)that can realize the organic combination of user portrait and product portrait,and on this basis,the personalized recommendation is realized through the multi-dimensional recommendation algorithm based on combination la-bel(PLP-Rank).In order to ensure the diversity and stability of the algorithm,this paper uses the"random erasure"method to control the flow of data during the iteration,so as to recommend more products that may be of interest to users and increase the stable operation time of the algorithm.To control the rationality of the number of iterations of the algorithm,this paper adds a double judgment condition to ensure that the number of iterations is kept within a certain range.Experimental results show that,compared with the PersonalRank series algorithms,the proposed algorithm can effectively improve the efficiency,accuracy,and stability of personalized recommendations without adding additional complexity,and can provide a more personalized,stable,durable and efficient customized experience for consumers with different needs.