Research On Information Product Personalized Bundling Recommendation Base On Collaborative Filtering

With the popularization of Internet and the development of E-Commerce, more and more enterprises and users accept the emerging business activity style of online trading. When the e-commerce website supply rich merchandise for users, users often get lost in a lot of information in merchandise which result in the information overload problem, and can not find the product they need. Thus, users waste a lot of time and energy. On the other hand, enterprise strive to attract users to visit the website, it should ensure the profit of selling the products. Personalized recommendation technology of data mining can provide personalized product of suitable bundling recommendation. It is an effective way to resolve above-mentioned questions by combining with data mining technology and product bundling strategy.This paper analyzed the marketing strategy of product bundling, elaborate the superiority, affective factors and merchandise's characteristics of product bundling. Because of information product's lower marginal cost, cheaper transaction cost and higher willingness to pay, it had more advantages than traditional physical product bundling. The paper also analyzed product bundling had different effects on enterprises and users.The time complexity of online recommendation was the key indicator to measure recommend algorithm in real recommendation system. To the traditional collaborative filtering algorithm's performance problem, the thesis analyzed the time complexity of online recommendation based on collaborative filtering of user clustering. It didn't like the traditional collaborative filtering algorithm compute the similarity between the target user and the basic user, but calculate the similarity between the target user and the cluster center of basic user. Therefore, it was able to generate faster recommend speed.To the recommend product problem of traditional personalized recommendation system only from user's point, the paper considered the profit factor into personalized recommendation system. The recommendation not only was able to meet the needs of user but also increase earnings of enterprises. Experiment also showed that the personalized recommendation system which added into element of profit was better than the traditional one in accuracy aspect, and gained higher profit than the traditional one. At last, the paper made the useful exploration on the construction of the personalized recommendation system of product bundling.