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Community question answering (CQA) has become the most important knowledge sharing and exchange platform on the Internet, effectively recommending massive questions raised by users to users who may answer them, and mining the questions that users are interested in is the core function of such platforms. Some expert recommendation algorithms for CQA have been proposed to improve the efficiency of platform answering, but most of the existing work focuses on matching user interests with question information, ignoring the dynamic change of user interests, which may seriously affect the quality of recommendation. This paper proposes an expert recommen-dation algorithm combining attention and recurrent neural network (RNN), which not only realizes deep feature coding of the question content, but also captures the dynamically changing user interest. First, the question encoder combines convolutional neural network (CNN) and Attention mechanism on the basis of pre-trained word embeddings to realize joint representation of deep feature of question title and bound topics. Then, the user encoder captures the dynamic interest using Bi-GRU model on the time series of user's historical answers to questions, and combines the user's fixed label information to represent the long-term interest. Finally, a recommendation result combining the user's dynamic interest and long-term interest is generated according to the similarity calculation of output vectors of two encoders. This paper conducts comparative experiments on different parameter configurations and different algorithms on real data from Zhihu. Experimental results show that the performance of the algorithm is significantly better than the current popular deep learning expert recommendation methods.

With the rapid popularization of the Internet, the amount of Internet news has increased dramatically. In this case, how to effectively find relevant reports that are more in line with a specific topic has become an urgent problem to be solved. To address this issue, a topic matching algorithm based on the fusion of key entities and text abstracts was proposed in this study. Firstly, the W² NER model was used for named entity recognition to extract key entities using features such as word frequency, TF-IDF, lexical cohesion word-word similarity, and word-sentence similarity. Secondly, the Pegasus model was used for text summarization, and the deep semantic features of news texts were obtained by combining the key entity features with the text summary features using BiLSTM. Next, the cross-attention mechanism was employed to enhance the interaction between the matching news articles by performing feature interaction. Finally, the deep semantic features of the news texts and the text interaction features were fused together to participate in the determination of text topic matching. Comparative experiments were conducted on real data from Sohu, and the results showed that the proposed algorithm achieved similar accuracy and precision compared to other algorithms, while recall and F1 score were improved. [ABSTRACT FROM AUTHOR]