Recommendation method based on deep social relationship

Abstract

The invention discloses a recommendation method based on deep social relationship, which comprises the steps of 1, constructing a rating matrix of users for an object and a social relationship matrixamong the users; 2, constructing an input layer in a one-hot coding mode; 3, obtaining a social embedded matrix by automatic coding according to the social relationship matrix among the users; 4, constructing an embedded layer through the input layer and the social embedded matrix; 5, constructing a collaboration layer through the embedded layer; and 6, inputting information of the collaboration layer into a hidden layer to obtain corresponding predicted score information, and thus performing object recommendation. According to the invention, the social relationship among the users can be fully utilized to solve a problem of data sparsity, excellent accuracy is ensured at the same time through establishing a complex deep neural network structure between users and objects, and thus accuraterecommendation is carried out for the users.

Description

technical field [0001] The invention relates to the field of personalized recommendation, in particular to a recommendation method based on deep social relationships. technical background [0002] With the advent of the era of information explosion, recommendation systems are used in various industries, including: movies, books, academic papers, etc. The recommendation system mainly recommends items for each user independently based on the user's past purchase records and search history, as well as the behavior of other users. [0003] Collaborative filtering algorithms are widely used in the field of recommendation systems. The collaborative filtering algorithm discovers the user's preferences by mining the user's historical behavior data, divides the users into groups based on different preferences, and recommends items with similar tastes. Although the collaborative filtering algorithm is widely used and has high precision, it has the problem of data sparsity. [0004]...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem of data sparsity in existing collaborative filtering algorithms, the present invention proposes a recommendation method based on deep social relationships, hoping to make full use of the social relationships between users to solve the problem of data sparsity, and at the same time establish a complex relationship between users and items The deep neural network structure ensures good accuracy, and then realizes accurate recommendations for users

Images