Customized recommendation method based on graphs

Abstract

The invention provides a customized recommendation method based on graphs and can effectively reduce influence of sparsity on the recommendation effect. According to the method, a first step, a hidden meaning model is utilized to calculate historical scoring records of users to acquire hidden relationships among users and among objects; a second step, similarity among the users is calculated by utilizing the hidden relationships acquired in the first step, similarity among the objects is calculated, and a user graph and an object graph are constructed for the similar users and the similar objects; a third step, a user-object graph model is constructed by utilizing a user graph model and an object graph model acquired in the second step and a bipartite graph of the users and the objects acquired through utilizing the historical scoring records of the users; and a fourth step, the access probability of objects without scoring record of each user is ordered in a descending mode by utilizing a random walk personalrank algorithm, and front N objects are acquired to form a recommendation list for recommendation to the users.

Description

Technical field [0001] The invention belongs to the technical field of recommendation methods, and relates to a graph-based personalized recommendation method. Background technique [0002] Recommender System (RS) is a system that can actively recommend products or items to users by using users’ preferences. It uses users’ historical data to discover user preferences and push items that may be of interest to specific users. , A good recommendation system can bring considerable economic benefits to businesses. [0003] The composition of a complete recommendation system must include three elements: user model, recommended object model, and recommendation algorithm. The recommendation algorithm is the core of the recommendation system. At present, the more mature recommendation algorithms mainly include: recommendation based on collaborative filtering, implicit semantic model, recommendation based on graph model, combined recommendation, etc. [0004] An invention similar to the pre...

AI Technical Summary

Problems solved by technology

[0007] (3) Running speed problem

[0012] 2) The length of the path connecting two vertices is relatively short;

[0017] There are mainly sparsity problems, cold start problems, scalability problems, etc. in the current recommendation system. The sparse problem is due to the large scale of data, less overlap between two users, and sparse rating data, which leads to the recommendation of the algorithm. severe drop in quality

The scalability problem is due to the fact that the calculation time of the recommendation algorithm increases sharply with the increase of the number of users and items.

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