Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Recommender types <s> Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated. <s> BIB001 </s> Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Recommender types <s> The limitations of traditional knowledge representation methods for modeling complex human behaviour led to the investigation of statistical models. Predictive statistical models enable the anticipation of certain aspects of human behaviour, such as goals, actions and preferences. In this paper, we motivate the development of these models in the context of the user modeling enterprise. We then review the two main approaches to predictive statistical modeling, content-based and collaborative, and discuss the main techniques used to develop predictive statistical models. We also consider the evaluation requirements of these models in the user modeling context, and propose topics for future research. <s> BIB002 </s> Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Recommender types <s> Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering. <s> BIB003 </s> Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Recommender types <s> This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations. <s> BIB004
Two types of recommender systems have been broadly recognized in the field related to the way systems gather and build user preference profiles: those based on users' explicitly stated preferences, called preference-based recommenders, and those based on users' navigation or purchase behaviors, called behavior-based recommenders BIB004 BIB001  . The first category of systems, where users actively state their preferences and provide feedback, will receive more attention in our survey, especially for the sections concerning preference elicitation and refinement. In the following, we describe three types of preference-based recommenders: rating-based, feature-based (which includes casebased, utility-based, knowledge-based and critiquing-based), and personality-based systems. In the second category of systems (i.e., the behavior-based recommenders), users' interactions with the system only become noticeable after the recommendation list has been generated and displayed. Therefore, we will consider these systems only from the point of view of how the users interact with the recommendation list. BIB002 provide a comprehensive overview of such recommender systems; other ways to classify recommender systems are discussed by BIB003 , and BIB004 .