Recommendation and Personalization: A Survey

Recommendation and personalization attempt to reduce information overload and retain customers. While research in both recommender systems and personalization grew mainly out of information retrieval, both areas have emerged from nascent levels to veritable and challenging research areas in their own right. Whereas no technical or sophisticated methodologies exist by which to build such systems, the field also lacks a comprehensive, yet manageable survey by which to study recommendation systems and personalization facilities. In this paper, we attempt to fill that gap by presenting a thematic approach toward studying recommendation and personalization. Specifically, we present three major representative personalization themes: recommendation; induction, exploration, and exploitation of social networks; and personalization of information access. We unify the presentation of the three themes which we have extracted from the rich landscape of recommender system and personalization research via a functional metaphor, where inputs and output to a function are identified in each theme and instantiated through a number of systems and projects visited. In addition, we examine how a number of systems implement the function through various operators and techniques. Finally, we cover several broadening aspects, such as targeting, privacy and trust, and evaluation, regarding recommendation and personalization and present visions of future directions and challenges.