text: In this paper, we study shortlists as an interface component for recommender systems with the dual goal of supporting the user's decision process, as well as improving implicit feedback elicitation for increased recommendation quality. A shortlist is a temporary list of candidates that the user is currently considering, e.g., a list of a few movies the user is currently considering for viewing. From a cognitive perspective, shortlists serve as digital short-term memory where users can off-load the items under consideration -- thereby decreasing their cognitive load. From a machine learning perspective, adding items to the shortlist generates a new implicit feedback signal as a by-product of exploration and decision making which can improve recommendation quality. Shortlisting therefore provides additional data for training recommendation systems without the increases in cognitive load that requesting explicit feedback would incur. We perform an user study with a movie recommendation setup to compare interfaces that offer shortlist support with those that do not. From the user studies we conclude: (i) users make better decisions with a shortlist; (ii) users prefer an interface with shortlist support; and (iii) the additional implicit feedback from sessions with a shortlist improves the quality of recommendations by nearly a factor of two.
  @cite_7: Recommender systems have already proved to be valuable for coping with the information overload problem in several application domains. They provide people with suggestions for items which are likely to be of interest for them; hence, a primary function of recommender systems is to help people make good choices and decisions. However, most previous research has focused on recommendation techniques and algorithms, and less attention has been devoted to the decision making processes adopted by the users and possibly supported by the system. There is still a gap between the importance that the community gives to the assessment of recommendation algorithms and the current range of ongoing research activities concerning human decision making. Different decision-psychological phenomena can influence the decision making of users of recommender systems, and research along these lines is becoming increasingly important and popular. This special issue highlights how the coupling of recommendation algorithms with the understanding of human choice and decision making theory has the potential to benefit research and practice on recommender systems and to enable users to achieve a good balance between decision accuracy and decision effort.
  @cite_29: We present the Visual Decision Maker (VDM), an application that gives movie recommendations to groups of people sitting together. The VDM provides a TV like user experience: a stream of movie stills flows towards the center of the screen, and users press buttons on remote controls to vote on the currently selected movie. A collaborative filtering engine provides recommendations for each user and for the group as a whole based on the votes. Three principles guided our design of the VDM: shared focus, dynamic pacing, and encouraging conversations. In this paper we present the results of a four month public installation and a lab study showing how these design choices affected people's usage and people's experience of the VDM. Our results show that shared focus is important for users to feel that the group's tastes are represented in the recommendations.
  @cite_32: This paper presents the design and study of interactive user modeling to support exploratory search tasks. Contrary to traditional interactions, such as query based search, query suggestions, or relevance feedback, interactive user modeling allows a user to perceive the state of the user model at all times and provide feedback that directly rewards or penalizes it. The technique allows the user to continuously tune the system's belief about the user's evolving information needs. We demonstrate that such functionality is useful in exploratory search where users need to get accustomed to a body of literature in a domain. We conducted two experiments where scientists carried out exploratory search tasks with our implementation of an interactive user modeling and retrieval system (SciNet) and two baselines: SciNet from which interactive user modeling was excluded and a real-world baseline (Google Scholar). The results show that interactive user modeling can help users to more effectively find relevant, novel and diverse information without compromises in task execution time.
  @cite_24: In today's "overloaded" information environment, deciding a stopping point for an exploratory search is not an easy decision. In this paper, we present the design and implementation of two search techniques: Result Preview (RP) and History Review (HR), to help people assess whether the information gained is enough and decide if he she should quit or continue. Both RP and HR are utilizing visual presentations to release people from the demanding cognitive requirements needed for an exploratory search. A formal user experiment with 24 participants is proposed to evaluate the benefits and limitations, and also inform the future RP and HR design.
  @cite_10: 
summary: Starting with HCI, there are a number of systems that were designed with the goal of aiding people in decision making. As there is a large body of work on general decision support systems @cite_10 , we only discuss work pertaining to search and recommendation @cite_7 . Ruetsalo @cite_32 propose a system for information retrieval tasks where a user model gets adapted during the search process, allowing the user to update feature weights after each query. We, in contrast, do not ask for explicit feedback in any form, but assume users are rational enough to only shortlist items that have relatively high utility. Also in information retrieval, Jia and Niu @cite_24 propose an interface that helps people know when to stop exploring. Drucker @cite_29 present a visual way of supporting movie selection in groups -- an interesting scenario we would like to like to study in the future. In contrast to their work, we propose shortlists not as an entire system to solve an end-to-end task, but rather as a component that provides digital memory. Hence, our approach can be seen as complementary to these systems -- one can imagine adding shortlists to them as an additional component.