Collaborative filtering recommendation method based on association rule prediction

Abstract

The invention discloses a collaborative filtering recommendation method based on association rule prediction, and relates to the field of personalized recommendation of the Internet. According to the feedback of searching and browsing behaviors of users, item coring data by the users are mined and obtained; the items which can not be mined are predicted by mining association rules between user characteristics and item characteristics; a scoring preference matrix of the users is constructed through the association rules between the users and the items; according to the preference matrix of the users, a sparse scoring preference matrix of the users is predicted; a traditional similarity measuring method is improved, the similarity between the user ui and the user uj is calculated, the most adjacent user BNS of a target user is obtained through the user similarity method, and recommended scores are obtained finally through the BNS; the collaborative filtering recommendation method based on association rule prediction is evaluated. According to the method, the accuracy for calculating the similarity between the users is effectively improved, high recommending quality is kept under the condition of sparse data, and the recommending quality for new users is also high in readiness.

Description

Technical field [0001] The invention relates to the field of Internet personalized recommendation, in particular to a collaborative filtering recommendation method based on association rules. Background technique [0002] Information overload is an important feature and main challenge of Internet applications. A large amount of information is released to the Internet every day, making traditional search technologies unable to meet users' needs for information discovery. The emergence of recommendation systems can better help users discover and obtain more information. Information tailored to individual needs. Personalized recommendation is based on user characteristics and personal preferences. Its main task is to push content of interest to target users from massive amounts of information. With the development of Internet technology and e-commerce, Amazon, Taobao, eBay, etc. have adopted different forms According to the requirements of different recommendation systems, res...

AI Technical Summary

Problems solved by technology

However, the calculation of association rules for mining item sets is relatively large, and there is also the problem of sparsity of user data, which reduces the accuracy of recommendation.

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