Microblog attention recommendation method based on parallel item-based collaborative filtering algorithm

Abstract

The invention discloses an microblog attention recommendation method based on a parallel item-based collaborative filtering algorithm. The method includes the steps of firstly, acquiring the microblog user interaction behavior data and microblog user attention behavior data; secondary, abstracting the microblog user interaction behavior preference data and microblog user attention behavior preference data from the above data and storing the data in an HDFS; then, adopting the MapReduce framework and the HDFS framework of the Hadoop to carry out the parallel design for the item-based collaborative filtering algorithm, and making mining analysis for the microblog user interaction behavior preference data and microblog user attention behavior preference data to find the item which is most similar with the item which is concerned by the user and filter the item which has been concerned by the user and recommending the item to a target user. The application of the item-based collaborative filtering algorithm under a non-traditional scene is realized, the displayed rating data is not depended on, and the recommendation individuation is improved.

Description

technical field [0001] The invention relates to a method for recommending microblog attention based on a parallel item-based collaborative filtering algorithm, and belongs to the technical fields of recommendation systems and data mining. Background technique [0002] With the popularity of the Internet and the rapid development of social networks, Weibo has become one of the most popular social applications. In the era of information overload, it is difficult for users to find the objects of interest (which can be people, institutions or groups) from the massive amount of information. Therefore, it is a crucial issue to grasp the user's interest points and recommend potential interested objects to the user in a targeted manner. Collaborative filtering algorithm is the most widely used algorithm in personalized recommendation system. Among them, the basic idea of ​​the item-based collaborative filtering algorithm is to find the user's related preferences according to the u...

AI Technical Summary

Problems solved by technology

However, although these recommended follow-up objects have good popularity, they do not consider the user's personal preferences, so they lack personalization.

[0004] With the rapid growth of microblog data, when using the item-based collaborative filtering algorithm to calculate the similarity between items and make predictions, the single-machine centralized calculation can no longer meet the recommendation system's requirements for recommendation efficiency

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