Heterogeneous information network based content providing method and system

Abstract

The invention discloses a heterogeneous information network based content providing method. When users subscribe to a recommendation service, content is recommended to the users through an optimal prediction matrix, a similarity matrix of the users and a similarity matrix of projects are obtained by an element path based similarity calculation method according to a heterogeneous information network through the optimal prediction matrix, the fusion is performed on two or three of the user similarity matrix, a user and project evaluation matrix and the project similarity matrix to obtain the internal relation between the users and the projects, the prediction is performed by a collaborative filtering based matrix decomposition prediction method, and results are combined to obtain the optimal prediction matrix. According to the heterogeneous information network based content providing method, the recommendation accuracy is effectively improved, the results which are accord with the will of the users can be recommended to the users through limited time calculation on the basis of the existing data, the cold start problem is partially solved, and the interpretability of the recommended results is improved.

Description

technical field [0001] The invention relates to a content providing method and system, in particular to a content providing method and system based on a heterogeneous information network. Background technique [0002] With the development of science and technology, we have now entered the era of information explosion. Due to the emergence of massive information, the utilization rate of information has been reduced, resulting in a phenomenon called information overload. Especially in the current era of big data, how to accurately find the information you need from the massive data is a very urgent problem. The recommendation system is an effective means to solve this problem. [0003] Collaborative filtering method is the earliest and most researched field in recommendation system, and has been widely used in the industry. The basic idea is that if users have the same preferences in the past, they will also have similar preferences in the future. Pure collaborative filteri...

AI Technical Summary

Problems solved by technology

Since rating matrices are generally very sparse, the main challenge of collaborative filtering is to get accurate predictions with relatively few valid ratings

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