ABSTRACT
This chapter discusses advances related to the cold start problem and presents approach to address the cold start problem. It examines the cold start problem of users with few ratings. The chapter presents a new recommender model to alleviate the user cold start problems of Recommender System (RS). It analyses the shortages of traditional Collaborative Filtering methods and proposes classified all users into groups on the basis of their preferences. The chapter explains the nearest neighbors of cold users using a similarity measure and also analyses popular items of cold users’ neighbors were recommended. The cold start issue is a serious problem in RS and is divided into the cold users’ problem and the cold items’ problem. The chapter describes a new model, named preference-based recommender, to solve the cold users’ problem. The hybrid recommender systems combining collaborative filtering with content-based or knowledge-based information are the most common methods.
