Design And Implementation Of Recommendation System To Questioner In Online Learning Community

With the advent of the Internet age,more and more learners are beginning to learn in online learning community.Learners learn knowledge by asking questions,answering questions,and collaborative learning.In order to help learners learn effectively,a large number of recommendation systems have been created.such as,the personalized recommendation based on the tag in zhihu,video recommendation of mooc,etc.These recommendation systems are designed to provide learners with courses,knowledge,and other recommendation services,at the same time,learners can learn interesting knowledge and solve their own questions effectively.Therefore,constructing a recommendation system based on the learning analysis background has become a research hotspot,These recommendations's purpose is recommending relevant questions,answers and answerers for the questions.The recommendation toward questioner in online learning community mainly include the recommendation of question's answer and answerers.The recommendation of question's answer refers to recommending relevant questions and answers for questioner.The recommendation of answerers refers to recommending question answerers for questioner.The purpose of recommending question's answers and answersers can effectively reduce the learner's learning wait time and promote the learner's learning success and satisfaction.In order to achieve the purpose of recommending relevant questions and answers and answerers to questioner,the main works done in this paper are as follows:First,based on the Scrapy crawler framework,we designed and implemented a crawler to obtain relevant data of learners.And then,preprocess and analyze the acquired data.Secondly,combining with clustering algorithm,we designed a algorithm to recommending question and answers for questioner.According to the interest,expertise,attention of answerer,we designed answerers model.And then,based on the model and acquired data,we designed an algorithm to recommending answerer for questioner.Finally,based on the above research and Python's Django framework,we constructed a recommendation system for questioners.Then,based on the acquired zhihu datasets and constructed recommendation system,relevant experiments and analysis are performed,The experiments results showed the effectiveness of constructed recommendation system in this paper.