Item recommendation method and system based on user-item bipartite model

Abstract

The invention discloses item recommendation method and system based on a user-item bipartite model. The method includes: extracting acquired user behavior information to establish a user-item bipartite model; establishing an item-to-user inverted list model on the basis of the user-item bipartite model, calculating item weights, and establishing a user-item weight list; calculating weights of item pairs corresponding to users and common user weights of the item pairs according to the established user-item weight, and establishing an item pair-common user weight list; operating to obtain an inter-item similarity list according to a preset similarity algorithm; querying the established user-item bipartite model to obtain recommended user-mapped items, querying an inter-item similarity list according to the user-mapped items to be recommended to obtain query result, and generating a recommendation list according to the query result. By the use of the method and system, calculation needed by recommendation can be reduced and recommendation efficiency can be improved.

Description

technical field [0001] The invention relates to computer information processing technology, in particular to an item recommendation method and system based on a user-item bipartite graph model. Background technique [0002] Personalized item recommendation refers to a network recommendation method that regularly recommends items to users by mining user behavior information such as user interests and historical user behavior. The personalized recommendation system based on personalized recommendation (recommended system for short) is an advanced intelligent platform based on the mining of massive user behavior information, which is used to provide users with fully personalized decision support and information services, and improve user experience. business experience. [0003] figure 1 It is a schematic flow chart of the existing item recommendation method based on the user-item bipartite graph model. see figure 1 , the process includes: [0004] Step 101, extracting use...

AI Technical Summary

Problems solved by technology

[0056] It can be seen from the above that the existing item recommendation method based on the user-item bipartite graph model needs to iterate in the entire user-item bipartite graph model when recommending items to users, which requires a large amount of calculation and takes up A large amount of temporary storage space; at the same time, more iterations are required, which not only increases the amount of calculations, but also greatly increases the time required for calculations, resulting in users taking a long time to obtain recommended items, and the recommendation efficiency is low

For example, for figure 2 The example shown contains only 7 nodes and 7 edges, and it takes 9 iterations to obtain the similarity value of the item nodes. In practical applications, the number of nodes and edges is large, and the amount of data is larger. The number of iterations required More, making the time complexity and space complexity of large data volume calculations higher, it is difficult to obtain recommendation results in real time, and the recommendation efficiency is reduced

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