topN recommendation method for social network based on cloud model

Abstract

The invention discloses a topN recommendation method for a social network based on a cloud model, and belongs to the field of data mining and information retrieval. The method comprises the steps: collecting recommended user information, scored object information and recommending user information through employing a social network mass data set; extracting user attributes and obtaining user score from the recommended user information and the recommending user information, and generating multi-dimensional clouds through a multi-dimensional cloud reverse cloud generator; calculating user attribute cloud similarity through the similarity of the multi-dimensional clouds; Generating a scoring cloud through the scores of the recommending user and the recommended user, combining the scoring clouds to generate a father cloud, and generating a new score in the father cloud; integrating the similarity and scores to generate the interest of the recommended user in new objects, and taking the former N objects with the high interest to generate a topN recommendation set. The method solves a problem that the uncertain setting of single or multiple attribute values brings obstruction to a recommendation system, generates different recommendation sets for different users in personal recommendation, enables the related recommendation information to be transmitted to the users, and saves the network resources.

Description

technical field [0001] The invention belongs to the field of data mining and information retrieval, relates to information collection and analysis of computer systems, and is a cloud model-oriented personalized recommendation method for social networks. Background technique [0002] With the rapid development of the Internet, people have gradually entered the era of information overload. In order to solve the problem of information overload, people continue to adopt new measures, such as strengthening search engines, optimizing recommendation systems, etc. to solve the problems caused by massive information. [0003] In recent years, recommendation systems have been increasingly favored by Internet of Things giants and e-commerce companies, especially the development of personalized recommendation technology, which has played an important role in improving user experience and service quality. Recommender systems can be divided into two categories: rating prediction and pers...

AI Technical Summary

Problems solved by technology

[0005] The present invention aims at the single attribute of the collection object and the uncertainty of the multiple weight settings in the collaborative filtering recommendation in the prior art, and satisfactory recommendation results cannot be obtained, resulting in too much useless information sent in the network, occupying channel resources, and easily causing network congestion, etc. technical problem

Aiming at the problem that the traditional similarity calculation is too single on a single attribute and the uncertainty of multiple attribute weight settings, a method for calculating the similarity of social network user behavior based on a multidimensional cloud model is proposed

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