Research On The User Cold-start Problem In Recommender System

In the data age,information in the cyberspace is overloaded.In the face of massive,repetitive,useful,useless information,it is difficult for users to choose the products that they are interested in.In order to solve this problem,scholars have put forward recommender system.Recommender system can alleviate the impact of the information overload problem by analyzing users' hobbies and needs,and recommending products,information or services that users are interested in to them.In the research process of recommender system,the theories of Collaborative Filtering algorithm,Content-Based recommendation,KnowledgeBased recommendation and Hybrid recommendation has been put forward.In addition,in order to more effectively solve the problem of information overload,continuous innovations of recommender theory have been proposed.What's more,technologies in data mining,machine learning,deep learning and other methods have been applied to recommender system.There are many challenges in the research of recommender system,such as data sparseness,the cold-start problem,large data processing in incremental calculation,and system fragility.The cold-start problem can be divided into the non-pure cold-start problem and the pure cold-start problem,that is,users that have just joined the system have less product evaluation records even no records.For these new users,it is extremely difficult to recommend products that are available to them.Due to the unavoidability of the cold-start problem and its influence on recommendation accuracy,in this thesis,the NPBM recommender model and the NDBM recommender model have been proposed to solve the problems of the non-pure cold-start and the pure cold-start,respectively.For the non-pure cold-start problem,recommender system is difficult to accurately analyze the product preferences of cold users,because cold users have only a few product evaluation records.This thesis proposes the NPBM recommendation model which based on the machine learning method,to solve the problem.The idea of the NPBM recommender model is to accurately locate the nearest neighbors of the cold user based on the users' product evaluation records,and then recommend the products that his nearest neighbors prefer to the cold user.For the problem of pure cold-start,recommender system can not analyze cold users' product preferences because they do not have any product evaluation records.To this end,this thesis proposes the NDBM recommender model which is based on the demographic theory to solve this problem.The NDBM recommender model uses the demographic characteristics to accurately find the similar users of cold users,and then recommend the products that these similar users like to the cold users.In this thesis,a large number of simulation experiments are carried out on real datasets.The experimental results show that the two recommender models have high recommendation accuracy when facing the cold-start problem.That is to say,our models can effectively alleviate the influence of the cold-start problem.