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A Calculation Method of User Event Relevance Based on Content Environment Enhancement

A technology related to events and computing methods, applied in computing, data processing applications, special data processing applications, etc.

Active Publication Date: 2017-10-10
ZHEJIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Existing recommendation methods cannot solve the recommendation problem of this kind of new objects well

Method used

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  • A Calculation Method of User Event Relevance Based on Content Environment Enhancement
  • A Calculation Method of User Event Relevance Based on Content Environment Enhancement
  • A Calculation Method of User Event Relevance Based on Content Environment Enhancement

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Embodiment Construction

[0081] The technical solution of the present invention will be further described in conjunction with specific implementation and examples.

[0082] Such as figure 1 , the specific implementation process and working principle of the present invention are as follows:

[0083] 1) The description information of a single user and the description information of a single social event are composed of user files and social event files in text form, and the topic model is used to reduce the dimensionality of user files and social event files into topic distribution vectors; by comparing a user file and the topic distribution of a social event file, the user preference features corresponding to the user file and the social event file are calculated.

[0084] A user profile includes descriptive information about the user and descriptive information about social events that the user has attended. Both the description information of the user and the description information of the social eve...

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Abstract

The invention discloses a user event relevance calculation method based on content environment enhancement. The method comprises the following steps: lowering the dimensions of archives of a user and social events by using a topic model, and converting the archives into topic distribution; calculating user preference features of the user archives and the social event archives; extracting online and offline social influence features based on a collaborative filtering method by taking the user preference features as probabilities of the fact that the user participates in corresponding events; acquiring local interested topic distribution according to events in a city where the user is located, and comparing the local interested topic distribution with the events to obtain local popularity features; acquiring user event relevance through a training study sequencing model. According to the method, content environment relevant information in an event social network is fully mined to extract the features of user preferences, social influences and local popularity for calculation, and a plurality of features are combined, so that the accuracy of a final result is increased, and the technical problem concerned with recommendation specific to the type of novel objects such as social events in a personalized recommending system is solved.

Description

technical field [0001] The invention relates to a computer language content processing method, in particular to a user event correlation calculation method based on content environment enhancement in the field of computer natural language processing. Background technique [0002] Topic modeling is a statistical method for extracting abstract topics in document collections. Latent Dirichlet allocation (LDA) is a commonly used topic model. It is based on the bag of words model, that is, a document is a collection of words, and there is no order between words and words. successive relationship. A document can contain multiple topics, and each word in the document is generated by one of the topics. The LDA method can generate the topic of each document in the document collection in the form of a probability distribution, and each topic can also be described in the form of a word distribution. Since the number of topics is usually much smaller than the number of words, the top...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
CPCG06F16/9535G06Q50/01
Inventor 寿黎但陈刚陈珂伍赛胡天磊王振华
Owner ZHEJIANG UNIV
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