Question and answer community recommendation method based on cross-platform tag fusion

Abstract

The invention provides a question and answer community expert recommendation method for performing interest modeling by use of tag fusion across platforms. According to the method, cross-platform common users are utilized to construct word vectors of tags by combining an LDA topic model and word2vec, a tag semantic similarity matrix is constructed for text data of different platforms, a fusion feature space is generated, and a fusion space model of the users is obtained. Compared with a single-network user model, the cross-platform user model can cover different features of the users more comprehensively, and the user features are described more clearly. Meanwhile, answer abilities of the users and cross-platform community influences of the users are comprehensively considered, a PageRank algorithm based on a fusion network is used to perform authority evaluation on the users, and then community feedback is considered to perform ability evaluation on the users. Through experiment comparison with a reference interest model, the single-network user model, a collaborative filtering recommendation model and other algorithms, it is shown that the algorithm has a better recommendation effect.

Description

technical field [0001] The invention relates to expert recommendation research in a question-and-answer community, and is an expert recommendation method based on cross-platform tag fusion. Background technique [0002] With the development of the Internet and informatization, the community Q&A system has become an important platform for users to obtain information on the Internet. Users can ask questions about the content they want to know through natural language, and other users in the community can answer them. Through natural language communication, the question answering system can provide users with good knowledge and information sharing, and more conveniently meet the information needs of users. With the increase in the number of users in the community Q&A system, the number of questions also increases. Many questions in the community cannot be answered for a long time, or the quality of the answers obtained is not high, which cannot meet the needs of the questions a...

AI Technical Summary

Problems solved by technology

[0003] At present, the expert recommendation algorithm in the question-and-answer community generally uses the user documents or network structure of a single community to model and recommend users, but the data of a single platform usually only contains part of the characteristics of the user, and cannot comprehensively build a user model

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