Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Feature-based systems <s> The understanding of consumer interaction with online EC Websites is one of the big current challenges for online marketers. The present paper investigates what drives and impedes Net customers to interact with a marketer's websites and, more specifically, with decision support interface systems (DSISs) employed in these sites to assist users when shopping or searching for high-involvement consumer goods. DSIS are considered as a predecessor or partially already integrator of more elaborated agent systems that may be used in future EC environments. It is shown that the design of today's DSISs is sub-optimal for both marketers and consumers, because it fails to motivate user interaction. One reason for this might be that little effort has been made to transfer the insights from consumer behavior to the design of EC user interfaces. The current paper aims to address this gap by proposing a number of new DSIS design principles, all of which are based on insights from traditional marketing search- and perceived risk theory. The main contribution of this paper is that it proposes a design approach for EC Websites that intuitively “makes the user model available to the user” [Shneiderman and Maes, 36]. <s> BIB001 </s> Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Feature-based systems <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> BIB002 </s> Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Feature-based systems <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> BIB003
Rating-based recommender systems apply historical data (i.e., ratings) to estimate user preferences. However, historical data might become obsolete over time. Another major category of recommenders models user profiles and produces personalized recommendations based on their specified needs on item features. This type of recommender system can also be referred to as content-based systems in conventional taxonomy BIB003 . However, one distinguishing characteristic of feature-based systems identified in this article is that they allow users to explicitly express their preferences on specific item features. Feature-based recommenders do not attempt to build long-term generalizations about their users, but rather base their advice on an evaluation of the match between a user's need and the set of options available BIB002 . They are suitable especially for recommending products infrequently purchased in a user's life or those demanding a significant financial commitment, also called as high involvement products in BIB001 , such as notebooks, cars, or digital cameras. In this category, four kinds of systems can be identified in the literature: case-based, utility-based, knowledge-based and critiquing-based. Generally speaking, the latter three systems are variants of case-based systems. Case-based systems can rely on utilitybased or knowledge-based technologies to assess the similarity between a case and a user's query. Critiquing-based systems are a form of case-based systems which operate in a reactive fashion. We briefly introduce each system in the following sections.