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Personalized user tag modeling and recommendation method based on unified probability model

A probabilistic model and user labeling technology, applied in the Internet field, can solve problems such as topic drift and incomplete application, and achieve the effect of improving accuracy

Active Publication Date: 2011-04-06
TSINGHUA UNIV
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Problems solved by technology

However, this method relies on the user to manually input some tags, and then the system automatically recommends other tags. It cannot be fully applied to problems that only have resources but no users have tagged them.
Not only that, since they only consider co-occurring data, there may be a problem of topic drift

Method used

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  • Personalized user tag modeling and recommendation method based on unified probability model
  • Personalized user tag modeling and recommendation method based on unified probability model
  • Personalized user tag modeling and recommendation method based on unified probability model

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Embodiment Construction

[0043] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0044] The present invention studies the user's labeling behavior and labeling purpose through the statistical analysis of actual data, and formalizes the personalized labeling problem of social labeling websites, wherein the labeling behavior is formalized into triplets, and each user's Interests are described as a topic distribution, while labels annotated on each resource are modeled as either a generic label or a user-specific interest-based label, both of which are learned in a probabilistic generative process. Among them, a unified probabilistic model (User-dependent Tagging Model, UdT model for short) is proposed to describe the user-based tagging behavior. This model estimates the general topic distribution and the group based on the user-specific topic distribution. Then, a tag recommendation method based on UdT model is designed, and ...

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Abstract

The invention discloses a personalized user tag modeling and recommendation method based on a unified probability model, comprising the following steps: S1, carrying out statistics on tagging behaviors of users on a social tagging site; S2, carrying out formal definition on questions tagged by the users; S3, establishing a topic model based on user tagging, wherein the topic model is a unified probabilistic model and called a UdT model; S4, establishing a frame of a tag recommendation system based on the UdT model, wherein the frame is recommended through learning the interest of the users and according to semantic information included in the interest; and S5, verifying the frame of the tag recommendation system. Experimental results show that by using the method of the invention, user interest can be effectively explored and the accuracy of tag recommendation can be improved.

Description

technical field [0001] The invention belongs to the technical field of the Internet, and in particular relates to a learning understanding and recommendation technology of personalized user tags in social tagging websites, specifically a method for modeling and recommending personalized user tags based on a unified probability model. Background technique [0002] Social tagging is a main feature of Web2.0, which allows users to freely tag various resources, such as web pages, academic papers and multimedia resources. Social tags can help users classify and query various types of information. At the same time, it has great value for many practical applications, including web search, expanded query, personalized search, and web resource classification and clustering. With the emergence and rapid development of social tagging sites, such as social tagging sites (Flickr, Picassa, YouTube, Plaxo), blogs (Blogger, WordPress, LiveJournal), encyclopedias (Wikipedia, PBWiki), microbl...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 唐杰张宁
Owner TSINGHUA UNIV
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