结合注意力与循环神经网络的专家推荐算法  

Expert Recommendation Algorithm Combining Attention and Recurrent Neural Network

作　　者：吕晓琦 纪科 陈贞翔[1,2] 孙润元[1,2] 马坤[1,2] 邬俊[3] 李浥东 LYU Xiaoqi;JI Ke;CHEN Zhenxiang;SUN Runyuan;MA Kun;WU Jun;LI Yidong(School of Information Science and Engineering,University of Jinan,Jinan 250022,China;Shandong Provincial Key Laboratory of Network Based Intelligent Computing,University of Jinan,Jinan 250022,China;School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]济南大学信息科学与工程学院,济南250022 [2]济南大学山东省网络环境智能计算技术重点实验室,济南250022 [3]北京交通大学计算机与信息技术学院,北京100044

出　　处：《计算机科学与探索》2022年第9期2068-2077,共10页Journal of Frontiers of Computer Science and Technology

基　　金：国家自然科学基金(61702216,61772231,61671048,61672262);山东省重大科技创新工程(2018CXGC0706)。

摘　　要：在线问答社区(CQA)已经成为互联网最重要的知识分享交流平台,将用户提出的海量问题有效推荐给可能解答的用户,挖掘用户感兴趣的问题是此类平台最核心功能。一些针对问答社区的专家推荐算法已经被提出用来提高平台解答效率,但是现有工作大多关注于用户兴趣与问题信息匹配,忽视了用户兴趣动态变化问题,可能会严重影响推荐质量。提出了结合注意力与循环神经网络的专家推荐算法,不仅实现了问题信息的深度特征编码,而且还能捕获动态变化的用户兴趣。首先,问题编码器在预训练词嵌入基础上结合卷积神经网络(CNN)和Attention注意力机制实现了问题标题与绑定标签的深度特征联合表示。然后,用户编码器在用户历史回答问题的时间序列上利用长短期记忆神经网络Bi-GRU模型捕捉动态兴趣,并结合用户固定标签信息表征长期兴趣。最后,根据两个编码器输出向量的相似性计算产生用户动态兴趣与长期兴趣相结合的推荐结果。在来自知乎问答社区的真实数据上进行了不同参数配置及不同算法的对比实验,结果表明该算法性能明显优于目前比较流行的深度学习专家推荐算法。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 recommendation 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.

关 键 词：社区问答(CQA) 专家推荐 深度学习 注意力机制 循环神经网络(RNN) 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]