Gaussian Mixture Model-Based Prediction Method of Social Network User Interest
A Gaussian mixture model and social network technology, applied in data processing applications, special data processing applications, instruments, etc., can solve problems such as dynamic changes, computational complexity, and delay difficulties, and achieve shortened use time, high prediction accuracy, and prediction The effect of short-term interest
Active Publication Date: 2017-04-12
FUZHOU UNIV
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[0003] However, given the characteristics of social data (such as huge volume, diversity, and varying data value), it is difficult to predict user interests with high precision while keeping computational complexity and latency within acceptable limits.
In addition, among user interest characteristics, short-term interests may change dynamically (such as being influenced by friends)
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[0044] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.
[0045] This embodiment provides a Gaussian mixture model-based social network user interest prediction method, such as figure 1 and figure 2 shown, including the following steps:
[0046] Step S1: Obtain user data from social networks;
[0047] Step S2: Extracting feature vectors from the acquired user data to generate a series of feature vectors;
[0048] Step S3: constructing a prediction model by using a Gaussian mixture model;
[0049] Step S4: Using the EM algorithm to optimize parameters and calculate prediction results.
[0050] In this embodiment, the step S1 specifically includes: acquiring microblog information published or forwarded by p microblog users as training data, acquiring microblog information published or forwarded by q microblog users as test data, acquiring r Popular Weibo categories and s popular Weibos in each popular Weib...
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The invention relates to a social network user interest predicting method based on a Gaussian mixture model. The method comprises the following steps that 1, user data are acquired from a social network; 2, feature vector extraction is performed on the acquired user data, and a series of feature vectors are generated; 3, a predicting model is built by adopting the Gaussian mixture model; 4, parameters are optimized by adopting an EM algorithm, and a predicting result is calculated. According to the social network user interest predicting method based on the Gaussian mixture model, the Gaussian mixture model is adopted, therefore, the higher predicting precision can be achieved, the using time is shortened, and the short-term interest of a user is effectively predicted.
Description
technical field [0001] The invention relates to the technical field of social network information analysis, in particular to a method for predicting social network user interest based on a Gaussian mixture model. Background technique [0002] The rapid diffusion of information and the convenience of social networking allow large numbers of users to share their daily activities, exchange opinions, or establish friendships with others. According to a report, the number of social network users worldwide was estimated at 2.33 billion at the end of 2017. Therefore, effective feature learning and interest prediction are of great significance not only to users (such as finding users with similar interests), but also to service providers (such as analyzing user behavior in a set of application scenarios to make personalized recommendations). [0003] However, given the characteristics of social data (such as huge volume, diversity, and varying data values), it is difficult to predi...
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IPC IPC(8): G06F17/30G06Q50/00
CPCG06F16/9535G06Q50/01
Inventor 郑相涵赖太平郭文忠
Owner FUZHOU UNIV