# Service-Aware Interactive Presentation of Items for Decision-Making
## 1 Introduction  
The classical ranked list of results presented by search engines poorly supports content interpretation because it cannot explain why the returned items are relevant to the submitted query, or to some of its terms, and how results are related to each other; see in [1]. In order to mitigate this issue, and to increase the acceptance of results, recent recommender systems [2] adopt presentation models aimed at supporting transparency [3] and trust [4, 5] by enriching the suggestions they generate with a description of the degree, or of the type of matching between users and items. For instance, RSR [6] shows the topic relations between user and proposed items, e.g., by research theme. Moreover, hybrid recommender systems propose analytic visualizations that show how strongly the embedded engines support the suggested items [7, 8, 9]. Furthermore, to support exploratory search [10], other works promote the development of faceted search interfaces that empower the user to control the information filtering process by guiding her/him in the selection of item features, e.g., see the seminal works by [PERSON] [11, 12] and recent ones such as in [13, 14]. We claim that current information presentation models offer limited support the exploration of online catalogs, because of the following. * Describing the rationale behind the suggestions generated by a system can enhance its transparency but it does not necessarily provide the user with the information (s)he needs to decide whether the proposed items are good or bad for her/him. This type of explanation has been traditionally applied in diagnostic expert systems [15] to substantiate their inferences, as a trust measure to help the user assess the validity of the reached conclusions. Moreover, it has been promoted to improve interactive systems in [16]. However, when selecting items, users might adopt multiple evaluation criteria [17; 18] which might differ from those applied by the recommender system. Therefore, explaining why an item is suggested is not sufficient to support people in decision-making. * Faceted search interfaces return items having the exact features or aspects specified by the user, e.g., the restaurants that offer outdoor seating or which serve good food. However, these interfaces poorly address evaluation dimensions that depend on the aggregation of multiple properties, e.g., product quality. * Decision-making cannot be restricted to information filtering because the experience with items can involve different stages of interaction with the provider, from their search to their delivery/fruition, all of which impact on satisfaction. Moreover, specifically concerning experience goods [19], which have to be used in order to be evaluated, previous consumers' opinions are a key type of data to be considered, see, e.g., in [20]. Indeed, the data about items available in online catalogs and services can be exploited to enhance user awareness and decision-making because it complements metadata with a rich source of information about consumer experience represented by item reviews. Thus, in order to address the limitations of the above listed approaches, we aim at developing a novel information presentation model that provides the user with a holistic perspective on search results, based on a direct analysis of previous consumers' experience rather than on an internal elaboration of low-level details representing the viewpoint of the system. Different from existing models, which attempt to explain why items fit the user, we aim at enabling her/him to make a first-hand opinion about the proposed solutions by efficiently inspecting consumer feedback, in an interactive way. In other words, we propose to treat results generation and presentation as separate concerns. This approach has two main advantages: on the one hand, it enhances the system transparency by making information presentation independent of its inferences, which might be error-prone.