在线问答社区——海川化工论坛的回答者推荐算法

推荐系统已经在开发者社区Stack Overflow以及知乎、百度知道等热门问答社区发挥了重要作用，也即将成为海川化工论坛提高问答效率的关键技术。海川化工论坛作为国内最大的化工问答社区，问题不能得到及时有效的解答主要由于2大难点：稀疏性和冷启动。本文提出一种融合DeepFM与矩阵分解的混合推荐方法。算法以DeepFM作为辅助算法，矩阵分解作为主算法，通过结合用户的个人特征与问题的自身特征为论坛中的新问题推荐合适的回答者，可有效解决社区中的问题冗余。通过计算测试集的均方根误差与平均绝对误差，进一步验证本文提出的方法在海川化工论坛的有效性和可行性。

Online Question-and-answer Community:Haichuan Chemical Forum Respondents Recommended Algorithm

Recommendation system has played an important role in developer community Stack Overflow, Zhihu, Baidu Know and other popular question and answer communities, and will become the key technology for Haichuan Chemical Forum to improve the efficiency of question and answer. As the largest chemical question-and-answer community in China, Haichuan Chemical Forum is unable to get timely and effective answers due to two major difficulties: sparsity and cold start. This paper presents a hybrid recommendation method combining DeepFM and matrix decomposition. The algorithm takes DeepFM as the auxiliary algorithm and matrix decomposition as the main algorithm. By combining the user’s personal characteristics and the problem’s own characteristics, it recommends suitable respondents for new problems in the forum, effectively solving the problem redundancy in the community. By calculating the root-mean-square error and mean absolute error of the test set, the validity and feasibility of the proposed method in Haichuan Chemical Forum are further verified.