A user preference and distance weighted clustering method incorporating time factors

Abstract

A user preference and distance weighted clustering method incorporating time factors is disclosed. The user-user attribute matrix constructed by the basic objective characteristics of users is introduced to alleviate the user cold start problem, and the sparsity problem is mainly improved by introducing project characteristics, because the characteristics of the project can reflect user preferences from the content aspect, so that the dimension of the matrix can be reduced; Item characteristics are introduced into user-item scoring to obtain a small-dimensional user-item attribute total scoring matrix. When constructing user-item attribute preference matrix with TF-IDF algorithm, item characteristics are introduced and the influence of user interest drifting with time is considered. Basedon the above three matrices, the weighted Euclidean distance is obtained, and then the weighted Euclidean distance is clustered by using K-Means algorithm. The experimental results on Movie Lens dataset show that this method has better recommendation quality and performance than other related algorithms.

Description

technical field [0001] The invention relates to a personalized recommendation algorithm, and specifically provides a clustering method that integrates user preferences of time factors and distance weighting. Background technique [0002] In recent years, with the development of information technology and Web2.0, the information on the Internet has experienced an unprecedented surge, and problems have followed, mainly including the problem of information overload and the problem that users cannot accurately select relevant information. The recommendation system is One of the effective tools to overcome the problem of information overload. The core of the recommendation system is to design the recommendation algorithm. Therefore, various recommendation algorithms have been proposed in academia. Currently, the main recommendation algorithms used include content-based recommendation algorithms, combination recommendation algorithms, and collaborative filtering recommendation alg...

AI Technical Summary

Problems solved by technology

[0010] The purpose of the present invention is to solve the problem of user cold start and data sparsity in the original collaborative filtering recommendation algorithm, optimize and improve the existing algorithm, and design a user preference and distance weighted algorithm that integrates time factors clustering method

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