Collaborative filtering method based on socialized label

Abstract

The invention discloses a collaborative filtering method based on socialized labels, which comprises the following steps that: (1) first, a tripartite graph is used to build models for three different node, i.e. a user, an article and the socialized label, and random walk algorithm is applied to each user to recommend top-N personalized articles; (2) in order to solve the problem of the sparsity of socialized labels (i.e. articles are always labeled by only a small number of labels) and noise brought by the subjective factors of the user, the invention provides a method for expanding the labeling of the articles with a lasso logistic regression model, i.e. labels related to the semantics are increased for each article, and the labels with noise are removed; and (3) the weight of the labels in the recommendation process is regulated. The collaborative filtering method based on socialized labels organically integrates the semantic information of the socialized labels to the description of the articles, uses the lasso logistic regression model to expand the labels, solves the sparsity and noise problems of the socialized labels so as to greatly improve the precision and the performance of a personalized recommendation system.

Description

technical field [0001] The invention relates to the field of personalized recommendation, in particular to a collaborative filtering method based on social tags. Background technique [0002] With the rapid development of network and multimedia technology, the number of images on the Internet has exploded. According to statistics, in 2008, Google has indexed 1 trillion web pages, including more than billions of image data. The simultaneous presentation of massive amounts of information, on the one hand, makes it difficult for users to find the parts they are interested in, and on the other hand, it also makes a large amount of information that few people care about become "dark information" in the network, which cannot be obtained by ordinary users. The personalized recommendation system establishes the binary relationship between the user and the information product, uses the existing selection process or the similarity relationship to mine the potentially interested objec...

AI Technical Summary

Problems solved by technology

[0011] 2) Use the lasso logistic regression model to extend the label of the item, that is, for each item, add a label related to its semantics, and remove those labels with noise to solve the noise and sparsity problems existing in social labels, That is, the item usually has only a small part of the label;

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