User closeness-based mixed recommending system and method

Abstract

The invention discloses a user closeness-based mixed recommending system and a user closeness-based mixed recommending method, which are functional in recommending interesting projects for users in social network sites. The system comprises a user closeness determining module, a user closeness-based recommendation result generating module, a collaborative filtering-based recommendation result generating module, a content-based recommendation result generating module, and a result integrating module. The method is carried out by steps of determining the user closeness, acquiring user closeness-based recommendation result, acquiring collaborative filtering-based recommendation result, acquiring content-based recommendation result, and integrating the results. According to the method, data in the social network sites are fully utilized to make up the disadvantage of a traditional recommending system, therefore, the system application has the advantages of strong practicality, high accuracy and convenient implementation.

Description

Technical field [0001] The present invention is a hybrid recommendation system and method based on user intimacy, and belongs to the field of network data mining in the computer field. Background technique [0002] Social Network Sites (SNS) is an online site that facilitates social interaction between people. SNS users can post photos, status, logs, etc. online, and other users can comment and forward these And so on, to enhance communication and communication between people, so as to achieve the purpose of social interaction. Current SNS generally have a recommendation function, and the purpose of the recommendation function is to recommend items that are of interest to users and may be accepted. On the one hand, this improves the user experience and enables users to find items of interest more quickly. On the other hand, from a business perspective, this can achieve the purpose of SNS marketing. Therefore, it is very important to choose an accurate and efficient recommendati...

AI Technical Summary

Problems solved by technology

However, due to the sparse and scattered characteristics of SNS data, the effect of simply using collaborative filtering-based recommendation or content-based recommendation is poor.

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