关键词: 产品服务系统, 协同过滤, BP神经网络, 余弦相似度, 空气净化器

Abstract: The product service system is a personalized solution provided by the enterprise for the user.To design the solution quickly and accurately,an improved collaborative filtering algorithm was proposed to recommend the solution according to the user's personalized requirements.In the traditional collaborative filtering algorithm,the user similarity calculation method did not consider the preference of users to the attributes of scheme and the different weights of attributes from different configuration perspectives.For this problem,an improved Person cosine similarity algorithm was proposed considering the objective weights of attributes for different schemes.BP neural network was used to calculate the objective weight of each attribute under different schemes.Users' preference for scheme attributes was considered to calculate the user similarity,which could accurately predict the target user's rating of scheme and recommend the best scheme under the condition of incomplete cold start.The scheme recommendation process of air purifier product service system was taken as an example to verify the proposed method could improve the accuracy of user similarity calculation and scheme recommendation.

Key words: product-service system, collaborative filtering, back propagation neural network, cosine similarity, air purifier