PMF-based microblog user interest prediction method

Abstract

The invention provides a PMF-based microblog user interest prediction method, which comprises the following steps: S1) obtaining original data of microblog user posting behavior, social circle information and social relationship; S2) carrying out automatic text marking on the original data of users, microblog user posting behavior and social circle information, establishing a user interest theme matrix and carrying out social relationship mining on the original data of the social relationship to obtain a social trust relationship matrix between users; S3) carrying out corresponding time-series modeling on the data obtained in the S2) to form a user posting behavior time-series model and a social circle information time-series model; and S4) substituting the user posting behavior time-series model, the social circle information time-series model and the social trust relationship matrix obtained in the S2) and the S3) into an SC-PMF prediction model to obtain a microblog user interest prediction result. The method integrates factors of user history behavior, user social trust relationship and user social circle blog information and the like, and solves the problem of cold start.

Description

technical field [0001] The invention belongs to the technical field of social network information analysis, in particular to a PMF-based microblog user interest prediction method. Background technique [0002] Regarding the interest prediction of Weibo users, a series of relatively mature methods are the probability matrix decomposition method based on the probability graph model. It can succinctly represent complex probability distributions, efficiently calculate marginal and conditional distributions, and easily learn parameters and hyperparameters in probability models, and the probability matrix decomposition method is often used to predict user interests and recommendations. [0003] In 2008, R. Salakhutdinov et al. proposed a probability matrix decomposition (PMF) method for traditional collaborative filtering algorithms that could not solve the recommendation and cold start problems of a large number of sparse data sets. Experiments on a large number of sparse unbala...

AI Technical Summary

Problems solved by technology

[0003] In 2008, R. Salakhutdinov et al. proposed a probability matrix decomposition (PMF) method for traditional collaborative filtering algorithms that cannot solve the recommendation and cold start problems of a large number of sparse data sets.

[0004] The above studies have ignored the impact of blog post information in the user's social circle on the user's future interest and behavior when establishing the microblog user interest prediction model.

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