Judith Masthoff

Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers

Watching television tends to be a social activity. So, adaptive television needs to adapt to groups of users rather than to individual users. In this paper, we discuss different strategies for combining individual user models to adapt to groups, some of which are inspired by Social Choice Theory. In a first experiment, we explore how humans select a sequence of items for a group to watch, based on data about the individuals’ preferences. The results show that humans use some of the strategies such as the Average Strategy (a.k.a. Additive Utilitarian), the Average Without Misery Strategy and the Least Misery Strategy, and care about fairness and avoiding individual misery. In a second experiment, we investigate how satisfied people believe they would be with sequences chosen by different strategies, and how their satisfaction corresponds with that predicted by a number of satisfaction functions. The results show that subjects use normalization, deduct misery, and use the ratings in a non-linear way. One of the satisfaction functions produced reasonable, though not completely correct predictions. According to our subjects, the sequences produced by five strategies give satisfaction to all individuals in the group. The results also show that subjects put more emphasis than expected on showing the best rated item to each individual (at a cost of misery for another individual), and that the ratings of the first and last items in the sequence are especially important. In a final experiment, we explore the influence viewing an item can have on the ratings of other items. This is important for deciding the order in which to present items. The results show an effect of both mood and topical relatedness.

A Survey of Explanations in Recommender Systems

This paper provides a comprehensive review of explanations in recommender systems. We highlight seven possible advantages of an explanation facility, and describe how existing measures can be used to evaluate the quality of explanations. Since explanations are not independent of the recommendation process, we consider how the ways recommendations are presented may affect explanations. Next, we look at different ways of interacting with explanations. The paper is illustrated with examples of explanations throughout, where possible from existing applications.

Group Recommender Systems: Combining Individual Models

This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modelling the users affective state. It summarizes results from previous research in this area. It also shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple criteria.

Designing and Evaluating Explanations for Recommender Systems

This chapter gives an overview of the area of explanations in recommender systems. We approach the literature from the angle of evaluation: that is, we are interested in what makes an explanation “good”, and suggest guidelines as how to best evaluate this. We identify seven benefits that explanations may contribute to a recommender system, and relate them to criteria used in evaluations of explanations in existing systems, and how these relate to evaluations with live recommender systems. We also discuss how explanations can be affected by how recommendations are presented, and the role the interaction with the recommender system plays w.r.t. explanations. Finally, we describe a number of explanation styles, and how they may be related to the underlying algorithms. Examples of explanations in existing systems are mentioned throughout.

In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems

This paper deals in depth with some of the emotions that play a role in a group recommender system, which recommends sequences of items to a group of users. First, it describes algorithms to model and predict the satisfaction experienced by individuals. Satisfaction is treated as an affective state. In particular, we model the decay of emotion over time and assimilation effects, where the affective state produced by previous items influences the impact on satisfaction of the next item. We compare the algorithms with each other, and investigate the effect of parameter values by comparing the algorithms’ predictions with the results of an earlier empirical study. We discuss the difficulty of evaluating affective models, and present an experiment in a learning domain to show how some empirical evaluation can be done. Secondly, this paper proposes modifications to the algorithms to deal with the effect on an individual’s satisfaction of that of others in the group. In particular, we model emotional contagion and conformity, and consider the impact of different relationship types. Thirdly, this paper explores the issue of privacy (feeling safe, not accidentally disclosing private tastes to others in the group) which is related to the emotion of embarrassment. It investigates the effect on privacy of different group aggregation strategies and proposes to add a virtual member to the group to further improve privacy.

Effective explanations of recommendations: user-centered design

This paper characterizes general properties of useful, or Effective, explanations of recommendations. It describes a methodology based on focus groups, in which we elicit what helps moviegoers decide whether or not they would like a movie. Our results highlight the importance of personalizing explanations to the individual user, as well as considering the source of recommendations, user mood, the effects of group viewing, and the effect of explanations on user expectations.

Layered evaluation of interactive adaptive systems: framework and formative methods

The evaluation of interactive adaptive systems has long been acknowledged to be a complicated and demanding endeavour. Some promising approaches in the recent past have attempted tackling the problem of evaluating adaptivity by “decomposing” and evaluating it in a “piece-wise” manner. Separating the evaluation of different aspects can help to identify problems in the adaptation process. This paper presents a framework that can be used to guide the “layered” evaluation of adaptive systems, and a set of formative methods that have been tailored or specially developed for the evaluation of adaptivity. The proposed framework unifies previous approaches in the literature and has already been used, in various guises, in recent research work. The presented methods are related to the layers in the framework and the stages in the development lifecycle of interactive systems. The paper also discusses practical issues surrounding the employment of the above, and provides a brief overview of complementary and alternative approaches in the literature.

Evaluating the effectiveness of explanations for recommender systems

When recommender systems present items, these can be accompanied by explanatory information. Such explanations can serve seven aims: effectiveness, satisfaction, transparency, scrutability, trust, persuasiveness, and efficiency. These aims can be incompatible, so any evaluation needs to state which aim is being investigated and use appropriate metrics. This paper focuses particularly on effectiveness (helping users to make good decisions) and its trade-off with satisfaction. It provides an overview of existing work on evaluating effectiveness and the metrics used. It also highlights the limitations of the existing effectiveness metrics, in particular the effects of under- and overestimation and recommendation domain. In addition to this methodological contribution, the paper presents four empirical studies in two domains: movies and cameras. These studies investigate the impact of personalizing simple feature-based explanations on effectiveness and satisfaction. Both approximated and real effectiveness is investigated. Contrary to expectation, personalization was detrimental to effectiveness, though it may improve user satisfaction. The studies also highlighted the importance of considering opt-out rates and the underlying rating distribution when evaluating effectiveness.

The use of technology to promote physical activity in Type 2 diabetes management: a systematic review

With increasing evidence available on the importance of physical activity in the management of Type 2 diabetes, there has been an increase in technology‐based interventions. This review provides a systematic and descriptive assessment of the effectiveness of technology to promote physical activity in people with Type 2 diabetes. For this review, technology included mobile phones and text messages, websites, CD‐ROMs and computer‐learning‐based technology, and excluded telephone calls.

Generating Referring Expressions: Making Referents Easy to Identify

It is often desirable that referring expressions be chosen in such a way that their referents are easy to identify. This article focuses on referring expressions in hierarchically structured domains, exploring the hypothesis that referring expressions can be improved by including logically redundant information in them if this leads to a significant reduction in the amount of search that is needed to identify the referent. Generation algorithms are presented that implement this idea by including logically redundant information into the generated expression, in certain well-circumscribed situations. To test our hypotheses, and to assess the performance of our algorithms, two controlled experiments with human subjects were conducted. The first experiment confirms that human judges have a preference for logically redundant expressions in the cases where our model predicts this to be the case. The second experiment suggests that readers benefit from the kind of logical redundancy that our algorithms produce, as measured in terms of the effort needed to identify the referent of the expression.