A Comparison of Katz-eig and Link-analysis for Implicit Feedback Recommender Systems

Abstract: Recommendations are becoming more and more important in a world where there is an abundance of possible choices and e-commerce and content providers are featuring recommendations prominently. Recommendations based on explicit feedback, where user is giving feedback for example with ratings, has been a popular research subject. Implicit feedback recommender systems which passively collects information about the users is an area growing in interest. It makes it possible to generate recommendations based purely from a user's interactions history without requiring any explicit input from the users, which is commercially useful for a wide area of businesses. This thesis builds a recommender system based on implicit feedback using the recommendation algorithms katz-eig and link-analysis and analyzes and implements strategies for learning optimized parameters for different datasets. The resulting system forms the foundation for Comordo Technologies' commercial recommender system.