Incorporating group recommendations to recommender systems: Alternatives and performance

F. Ortega, J. Bobadilla, A. Hernando, A. Gutiérrez
2013 Information Processing & Management  
ABOUT THIS WORK This PhD thesis is the result of several years of research in the area of recommender systems. As result of this work, 15 papers indexed in SCI-JCR and 1 publication in a congress has been published. In this PhD thesis I present the results of one of the most promising papers. Group recommendations have started to expand due to the increasing use of social networks. This type of recommendations poses new challenges to researchers, since it adds more complexity to the
more » ... on problem. The following list contains all my publications related to recommender systems: ABSTRACT The importance of recommender systems has grown exponentially with the advent of social networks. In this PhD thesis I will provide a wide vision about the state of the art of recommender systems. They were initially based on demographic, contentbased and collaborative filtering. Currently, these systems incorporate some social information to the recommendation process. In the future, they will use implicit, local and personal information from the Internet of Things. As we will see here, recommender systems based on collaborative filtering can be used to perform recommendations to group of users. Previous works have made this modification in different stages of the collaborative filtering algorithm: establishing the neighborhood, prediction phase and determination of recommended items. In this PhD thesis I will provide a new method that carry out the unification process (many users to one group) in the first stage of the collaborative filtering algorithm: similarity metric computation. I will provide a full formalization of the proposed method. I will explain how to obtain the k nearest neighbors of the group of users and I will show how to get recommendations using those users. I will also include a running example of a recommender system with 8 users and 10 items detailing all the steps of the method I will present. The main highlights of the proposed method are: (a) it will be faster (more efficient) that the alternatives provided by other authors, and (b) it will be at least as precise and accurate as other studied solutions. To check this hypothesis I will conduct
doi:10.1016/j.ipm.2013.02.003 fatcat:vns4q3dex5fhfmao774o3dhasm