Similarity propagation and popularity dimensionality reduction based mixed recommendation method

Abstract

The invention relates to a similarity propagation and popularity dimensionality reduction based mixed recommendation method. According to the similarity propagation and popularity dimensionality reduction based mixed recommendation method, sparse data are processed in two phases; firstly, neighbors of the sparse data are expanded due to constant iteration of similar matrixes of users, resources and Tags through a similarity propagation method and accordingly elements for zero are filled; then a score algorithm in a search engine is introduced to calculate the Tag popularity in consideration of the problem that original data is provided with meaningless rubbish Tags, the tags with the popularity smaller than a certain threshold value are deleted to simplify data to perform dimensionality reduction on the matrix; recommendation results are diversified and the sparsity and cold starting problem can be relieved to some extent due to the fact that the recommendation based on contents and the collaborative filtering recommendation are combined. The similarity propagation and popularity dimensionality reduction based mixed recommendation method has the advantages of solving the problem of data sparsity in the individual recommendation process and being high in recommendation result accuracy, high in accuracy and high in reliability.

Description

technical field [0001] The invention relates to a personalized recommendation technology of data mining, in particular to a hybrid recommendation method based on similarity propagation and popularity dimension reduction. Background technique [0002] In the research of personalized recommendation algorithms, social tags, as an important display scoring technology, can not only describe resources but also represent user preferences, so recommendations combined with social tags are becoming a research hotspot in Internet recommendation engines. However, most researches on recommendation algorithms are faced with the problem of data sparsity. At present, the research methods to solve the sparsity problem are mainly divided into two categories: filling the matrix with various techniques and reducing the dimensionality of the data. [0003] In terms of filling the matrix, the simplest matrix filling method is to set the user's unrated items to a fixed default value, which can be...

AI Technical Summary

Problems solved by technology

[0005] The present invention aims at the problem of data sparsity in the recommendation algorithm, proposes a hybrid recommendation method based on similarity propagation and popularity dimension reduction, and provides a solution to the data sparsity problem in the personalized recommendation process, This method has high precision for recommendation results, and has the advantages of high accuracy and high reliability.

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