Project recommendation method and system

Abstract

The application of the invention discloses a project recommendation method and a project recommendation system. The method comprises the steps of acquiring history data of a user, wherein the history data comprises a corresponding relation between the user and the projects; calculating an association retrieval relevance of any two projects according to the history data; as to each project, respectively determining a preset first amount of projects, which have the maximal association retrieval correlation with the project, as association retrieval related projects of the project; building an original user-project scoring matrix according to the history data of the user; zeroing the original user-project scoring matrix by the association retrieval related projects of each project to form a calculation user-project scoring matrix; and determining a project recommendation collection of the user based on the calculation user-project scoring matrix. According to the method and the system, the accuracy of the recommendation result is improved, and therefore, data transmission speed between an electronic commerce website and a user terminal is boosted further.

Description

technical field [0001] The present application relates to item recommendation technology, in particular to an item recommendation method and system. Background technique [0002] When users enter an e-commerce website to choose books, videos and other products, they are confronted with a large number of products. It is difficult to find products that meet their unique interests only by searching and browsing. Therefore, the personalized recommendation of products can solve the information overload of users and provide users with products that meet their interests. [0003] The historical roots of personalized recommendation research can be traced to research work in the fields of cognitive science, approximation theory, information extraction, and prediction theory, as well as related to management science and customer opportunity modeling in marketing. However, recommender systems emerged as an independent research field in the mid-1990s. Since then, recommender system re...

AI Technical Summary

Problems solved by technology

For other item recommendation systems, there is also the problem of inaccurate recommendation results similar to the book recommendation system

[0021] Based on the above situation, without considering the cost of building an item recommendation system for an e-commerce website, the inaccurate item recommendation results will also lead to the following problems: the user terminal and the e-commerce website need to request and send item recommendation data, The item recommendation data is not useful information for most users, so users still need to determine the items they are interested in through original search and browsing according to their own interests, so when the number of users is large, this Data transmission will inevitably waste the data transmission bandwidth of the e-commerce website, thereby reducing the data transmission speed between the e-commerce website and each user terminal, thereby reducing the response speed and efficiency of the e-commerce website to the user terminal

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