A Blog Post Recommendation Method for Weak-Relationship Social Networks

Abstract

The invention discloses a method for recommending blog posts for a weak-relationship social network. The method includes: using users and blog post data in the social network to divide active user groups and inactive user groups according to the activity degree, and then facing the active and non-active user groups respectively Active user groups, using the recommendation method based on the retweet network and the recommendation method based on the content tag to output the user-blog interest degree, and finally through the combination of the results, the user-blog interest degree value is obtained. The present invention uses the forwarding network, which is a method to capture the relationship between users more dynamically than the social network, and introduces two indicators of similarity and trust to describe the strength of the relationship between users, improving the traditional method based on Collaborative filtering recommendation algorithm for users. At the same time, for inactive users, the data sparsity and cold start problems of the forwarding network recommendation method are solved by using the recommendation method based on content tags.

Description

technical field [0001] The invention relates to the field of information recommendation in social networks, in particular to a blog post recommendation method for social networks with weak relationships. Background technique [0002] At present, there are many methods used in the field of recommendation, such as content-based recommendation algorithm (Content-basedFiltering), user-based collaborative filtering algorithm (User-based Collaborative Filtering), item-based collaborative filtering algorithm (Item-based Collaborative Filtering), etc. . But it faces many challenges, such as: data sparsity (Data Sparsity), cold start problem (Cold Start), timeliness and other issues. Existing recommendation algorithms have great difficulties in solving data sparsity and cold start. In order to solve these problems and pursue higher efficiency and better recommendation results of recommendation algorithms, both home and abroad have issued high bonuses to crowdsourcing , to improve t...

AI Technical Summary

Problems solved by technology

But it faces many challenges, such as: data sparsity (Data Sparsity), cold start problem (Cold Start), timeliness and other issues

Existing recommendation algorithms have great difficulties in solving data sparsity and cold start. In order to solve these problems and pursue higher efficiency and better recommendation results of recommendation algorithms, both home and abroad have issued high bonuses to crowdsourcing , to improve the accuracy of the recommendation algorithm

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