A Big Data-Based Optimization Method for Movie Semantic Personalized Tags

Abstract

The invention discloses a method for optimizing movie semantic personalized tags based on big data. By mining movie review data, alienated personalized movie tags are obtained; at the same time, text and vocabulary are obtained through neural network model vectorization, and the similarity of movie introduction text is obtained. The similarity between degree and label vocabulary, combined with the deviation of custom labels before and after optimization, establishes a machine learning model, and initializes the machine learning model through personalized labels. The present invention realizes the optimization of existing custom tags for movies, realizes merging redundant tags, corrects wrong tags, complements missing tags, and completes personalized tags; scientifically and effectively classifies and describes movie resources, and provides movie information retrieval Based on the basis, it solves a series of problems caused by artificial movie labels.

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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