Film semantics personalization tag optimizing method based on big data

Abstract

The invention discloses a film semantics personalization tag optimizing method based on big data. The method comprises the steps that foreignized personalization film tags are obtained by mining film criticism data; meanwhile, texts and words are obtained through vectorization of a neural network model, a machine learning model is built according to the similarity of the film synopsis texts, the similarity of the tag words and deviations between custom tags obtained before optimization and custom tags obtained after optimization, and the machine learning model is initialized through the personalization tags. By means of the optimizing method, the optimization of the existing custom tags of films is achieved, and it is achieved that redundant tags are combined, wrong tags are corrected, missing tags are complemented and the personalization tags are complemented; film resources are scientifically and effectively classified and described, a film information retrieval basis is provided, and a series of problems caused by artificial film tags are solved.

Description

technical field [0001] The invention relates to the technical field of big data analysis, in particular to a method for optimizing movie semantic personalized tags based on big data. Background technique [0002] Stimulated by the development of the film and television industry and people's spiritual needs, the types and numbers of movies are increasing day by day, so the description of movies is becoming more and more important. At the same time, with the rapid development of the Internet, more and more shared information appears on various websites. For movies, there are Douban, Tencent and other websites. These sites allow users to comment and define category tags for different movies, not only as a kind of information sharing, but most importantly, it will optimize the process of searching for a specific video in the massive video library. However, with the rapid increase of Internet data, some problems arise, mainly as follows: [0003] First, there is the issue of th...

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Problems solved by technology

Because the movie tags on these websites come from user-defined, anyone can define tags on any resource under any circumstances, so these tags are arbitrary and imprecise, and are likely to cause problems such as contradictions and confusion, lacking correctness and reasonableness. Tags will make users get lost in redundant and complicated search results

[0004] Second, there is no personalized label

At present, the tags of movies are concentrated on the general tag set. A specific movie lacks a unique tag, which cannot accurately describe the movie. At the same time, this will make it impossible to search for a specific movie through a unique tag.

[0005] Finally, the waste of data resources, in the existing label optimization methods, very few methods take into account the introduction of the movie, while ignoring a large number of comment resources on the Internet, these resources are also a description of the movie, which will lead to Serious waste of content resources

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