Social information recommendation algorithm and system based on deep network embedding feature

Abstract

The invention discloses a social information recommendation algorithm and system based on a deep network embedding feature. A network embedding model is trained on the social network of a user in advance to extract network feature representation of the user, the extracted network feature is integrated into a matrix decomposition model; and a final generated model is used for score prediction and project recommendation. The social information recommendation algorithm and system based on the deep network embedding feature in the invention can not only make in-depth use of the social network information, but also can use a collaborative filtering model for recommendation as well as have better information recommendation accuracy and convergence.

Description

technical field [0001] The invention relates to a social information recommendation algorithm and system based on deep network embedded features. Background technique [0002] While the rapid development of computer technology provides users with convenient communication methods, it also puts users in the predicament of information overload. How to help users obtain valuable information from large amounts of data has become an urgent problem to be solved. Recommender systems, as one of the effective information filtering techniques, have attracted much attention in recent years. [0003] Traditional recommendation systems use matrix factorization (MF)-based collaborative filtering models to model user behavior. For example, Koren et al. used MF-based methods in the Netflix Prize competition and achieved better performance than traditional nearest neighbor techniques. . Salakhutdinov et al. further derived the probabilistic form of MF and proved that MF-based methods can a...

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Problems solved by technology

However, most of these existing studies consider the impact of social relations on users as a regular term o

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