Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Recommendation presentation <s> In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.Our work builds upon prior research on recommender systems, looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evaluate our method using book recommendation data, including offline analysis on 361, !, 349 ratings and an online study involving more than 2, !, 100 subjects. <s> BIB001 </s> Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Recommendation presentation <s> Recommender systems have shown great potential to help users find interesting and relevant items from within a large information space. Most research up to this point has focused on improving the accuracy of recommender systems. We believe that not only has this narrow focus been misguided, but has even been detrimental to the field. The recommendations that are most accurate according to the standard metrics are sometimes not the recommendations that are most useful to users. In this paper, we propose informal arguments that the recommender community should move beyond the conventional accuracy metrics and their associated experimental methodologies. We propose new user-centric directions for evaluating recommender systems. <s> BIB002 </s> Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Recommendation presentation <s> Based on our recent work on the development of a trust model for recommender agents and a qualitative survey, we explore the potential of building users' trust with explanation interfaces. We present the major results from the survey, which provided a roadmap identifying the most promising areas for investigating design issues for trust-inducing interfaces. We then describe a set of general principles derived from an in-depth examination of various design dimensions for constructing explanation interfaces, which most contribute to trust formation. We present results of a significant-scale user study, which indicate that the organization-based explanation is highly effective in building users' trust in the recommendation interface, with the benefit of increasing users' intention to return to the agent and save cognitive effort. <s> BIB003
Recommendation results are the set of items a recommender system produces and presents to an active user. In most cases, such results are labeled and displayed in a particular area of the screen (the "North", "South", and "East" sections of the screen are the most common areas). Users are likely to review recommended items as a set rather than reviewing them as individual items. In another words, reviewing one item from the set influences how the user reviews the other items BIB002 BIB001 . Because presentation is a crucial factor in persuading users to accept the items recommended to them, each recommender system must carefully employ special strategies that are sensible to users' information needs as well as the business goals of the RS. For example, would the result set include only highly-ranked items, but similar ones? Or would it include a well-balanced top-ranked one and popular items? Should it intentionally include familiar items that the system predicted for users? Should a recommender explain the recommendation's inner logic? According to prior work, users' perception of recommendations is directly related to their trust in the system BIB003 . Trust can further be seen as a long-term relationship between the user and the organization that the recommender system represents. Therefore, recommended results could affect users' confidence in whether the system is providing increasingly useful information. If the recommendation succeeds in inspiring user confidence and motivation to expend more interaction effort, users will increasingly like the system. In the following sections, we will discuss these presentation issues in greater detail.