一种基于Skill-LFM的知识点推荐方法

A method of knowledge item recommendation based on Skill-LFM

目前,知识库的用户主要是通过检索获取所需知识点,这种依赖搜索引擎解决信息过载的方法,对实时在线服务而言效率低下,对离线知识学习来说不具有完整性和连续性,为此提出由知识库系统根据用户技能水平主动推荐知识点给用户,提高决策效率,并有助于用户建立完备的知识学习体系.基于用户对知识点的历史行为以及用户对知识的学习能力,提出一种融合技能的隐语义模型的协同过滤推荐方法,将知识点难易程度作为潜在因子,同时考虑用户的能力水平预测用户对知识点的偏好水平.在呼叫中心知识库的数据集上进行测试,其均方根误差优于基础隐语义模型.综合知识点推荐的应用领域和知识学习行为数据的特点,对于知识点推荐方法,可从融合用户和知识点上下文信息的推荐技术上深入研究.

At present,the users of knowledge base mainly get the required knowledge items through search,which relies on the search engine to solve the information overload problem.It is inefficient for real-time online services,and has no integrity and continuity of offline knowledge learning.Therefore,it is proposed that knowledge items should be actively recommended to users by the knowledge base system according to their level of skills,to improve the efficiency of decision making,and also to help users establish a complete knowledge learning system.A collaborative filtering recommendation method is proposed to predict every user's preference on knowledge items,based on the historical behavior of a user on the knowledge items,and the knowledge learning ability of this user.This method combines latent factor model with skill,named Skill-LFM,where the difficulties of knowledge items are taken as potential factors,and users'ability level is considered to give personalized recommendations.Tested on the data from a call center knowledge base,the proposed Skill-LFM outperforms the baseline latent factor model in terms of lower RMSE.Considering the characteristics of the application domain and the historical behavior data of the knowledge base,this paper demonstrates the possibility of further improving knowledge item recommendation through integrating user and knowledge item context information.