The use of Self-Organizing Maps in Recommender Systems : A survey of the Recommender Systems field and a presentation of a State of the Art Highly Interactive Visual Movie Recommender System
Sam Gabrielsson, Stefan Gabrielsson · 2006
“Is one’s entire psyche’s most secret landscape really a fairly public thing, given just a few pieces of information? ” – Douglas R. Hofstadter This is a thesis about recommender systems, and the different approaches recommender systems use to solve the information overload problem. Our focus lies on two common approaches, collaborative filtering and content based filtering. Both of these approaches have their weaknesses and strengths. To overcome the weaknesses of each approach, various hybrid filters have been developed. We will start by analyzing these three approaches based on previous research literature and will then proceed to implement different variants of these approaches, including our own filtering approach for the movie domain. These implementations will be done in Java and open sourced for further development by other researchers in this area. The results will be evaluated and compared against previous research in this area in order to validate our implementations. Evaluation will be done by using standard metrics that are commonly used for evaluating the accuracy of recommender systems. Various algorithms from the machine learning community have been used in the effort to improve and solve some of the problems in the previously mentioned approaches. We will concentrate on one