关键词: 异构信息网络, 问答社区, 协同过滤, 因子分解机

Abstract: With the development of Internet, computer and other technologies, Internet has brought various network services for users to enhance communication among users. Among them, the community question answering provides users with a communication platform for questions and answers, the purpose of which is to achieve knowledge and experience sharing and information dissemination among users through Internet. However, there are still some problems that limit the development of the community question answering. For example, as the number of users continues to increase, a large number of questions cannot be answered in time and the questioners are not satisfied with the answers to existing questions. Therefore, for the question and answer community, how to find experts from a large number of users is very important. In response to the above problems, this paper proposed a recommendation method based on heterogeneous information network. Firstly, a heterogeneous information network was established for the question and user attributes in the question and answer community, and meta-paths were used to capture the rich semantic information in the heterogeneous information network. Secondly, the similarity calculation method based on the meta paths was used to calculate the similarity matrix between the question and the user, and three methods were used to fuse the obtained similarity matrix with the question-user scoring matrix. In the end, the matrix decomposition was used to obtain the potential features of the question and the user. Factorization machine is used for training and recommendation. The method proposed in this paper was compared with various advanced recommendation algorithms on Haichuan chemical community question answering data set, and the evaluation index was used to evaluate the model. Experimental results show that the algorithm proposed in this paper has certain advantages over the previous algorithms in terms of relevant evaluation indicators.

Key words: heterogeneous information network, community question answering, collaborative filtering, factorization machine