融合多头自注意力的问答社区专家推荐算法

专家推荐是在线问答社区的研究热点之一，但现有的算法大多关注用户的静态兴趣和问题信息的匹配，忽视了对用户的动态兴趣表征信息的有效捕捉，从而导致推荐的准确度不足。针对上述问题，提出了融合多头自注意力的问答社区专家推荐算法。首先，构造由卷积神经网络和注意力机制组成的问题编码器，来处理目标问题和用户历史回答问题，提取对应的问题表征；其次，将用户历史回答问题序列当作时间序列，利用多头自注意力机制学习序列中所蕴涵的动态兴趣表征，结合用户的静态兴趣表征，获取用户的综合兴趣表征；最后，将目标问题表征和用户综合表征进行相似性计算产生推荐结果。利用来自知乎问答社区的真实数据进行了不同参数配置及不同算法的对比实验，实验结果表明该算法性能要明显优于目前较流行的深度学习专家推荐算法。

Expert recommendation algorithm for Q&A community based on multi-head self-attention

Expert recommendation is one of the research hotspots in the online Q&A community. However, the majority of the currently used algorithms concentrate on matching user''s static interest and question information, ignoring the efficient capture of user''s dynamic interest representation information, which results in a lack of recommendation accuracy. In order to address the aforementioned issues, this paper proposed an expert recommendation algorithm for the Q&A community that incorporated multi-head self-attention. Firstly, this algorithm built out question encoders of convolutional neural networks and attention mechanisms to process target question and user''s historically answered questions, in order to extract the corresponding question representations. Secondly, it treated the sequence of user''s historically answered questions as a time series and used the multi-head self-attention mechanism to learn the dynamic interest representations hidden in the sequence, and combined with the user''s static interest representations to obtain the user''s comprehensive interest representations. Finally, it calculated the similarity between the target question representation and the comprehensive user representation to generate recommendation results. Based on the real data from the Zhihu Q&A community, the experimental results show that the performance of this algorithm is significantly better than the current popular expert recommendation algorithm.