Judith Masthoff

Bio: Judith Masthoff is an academic researcher from Utrecht University. The author has contributed to research in topics: Personality & Computer science. The author has an hindex of 30, co-authored 180 publications receiving 4939 citations. Previous affiliations of Judith Masthoff include University of Aberdeen & University of Cambridge.

A Survey of Explanations in Recommender Systems

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

Abstract: 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.

Group Recommender Systems: Combining Individual Models

Abstract: 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.

Method for enabling a user to fetch a specific information item from a set of information items, and a system for carrying out such a method

Abstract: A user of a system comprising a large set of information items, for example a multimedia database, is assisted by an agent in searching the set. The agent has a given affinity for the information items and selects a specific information item from the set in conformity with said affinity. On the basis of the interactions between the user and the system an image is formed of the affinity of the user for the information items. The agent utilizes this image, in addition to its own affinity, for the selection of a specific information item. A major application of the invention concerns a system in which the information items are presented as objects in a space and in which the agent guides the user through the space and proposes a specific object to the user.

Designing and Evaluating Explanations for Recommender Systems

Abstract: 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.

经验与教育 = Experience and education

Abstract: Experience and Educationis the best concise statement on education ever published by John Dewey, the man acknowledged to be the pre-eminent educational theorist of the twentieth century. Written more than two decades after Democracy and Education(Dewey's most comprehensive statement of his position in educational philosophy), this book demonstrates how Dewey reformulated his ideas as a result of his intervening experience with the progressive schools and in the light of the criticisms his theories had received. Analysing both "traditional" and "progressive" education, Dr. Dewey here insists that neither the old nor the new education is adequate and that each is miseducative because neither of them applies the principles of a carefully developed philosophy of experience. Many pages of this volume illustrate Dr. Dewey's ideas for a philosophy of experience and its relation to education. He particularly urges that all teachers and educators looking for a new movement in education should think in terms of the deeped and larger issues of education rather than in terms of some divisive "ism" about education, even such an "ism" as "progressivism." His philosophy, here expressed in its most essential, most readable form, predicates an American educational system that respects all sources of experience, on that offers a true learning situation that is both historical and social, both orderly and dynamic.

Bowling alone: The collapse and revival of American community

Abstract: The present historical moment may seem a particularly inopportune time to review Bowling Alone, Robert Putnam's latest exploration of civic decline in America. After all, the outpouring of volunteerism, solidarity, patriotism, and self-sacrifice displayed by Americans in the wake of the September 11 terrorist attacks appears to fly in the face of Putnam's central argument: that \"social capital\" -defined as \"social networks and the norms of reciprocity and trustworthiness that arise from them\" (p. 19)'has declined to dangerously low levels in America over the last three decades. However, Putnam is not fazed in the least by the recent effusion of solidarity. Quite the contrary, he sees in it the potential to \"reverse what has been a 30to 40-year steady decline in most measures of connectedness or community.\"' As an example of how the current \"war on terrorism\" could generate a durable civic renewal, Putnam points to the burst in civic practices that occurred during and after World War II, which he says \"permanently marked\" the generation that lived through it and had a \"terrific effect on American public life over the last half-century.\" 3 If Americans can follow this example and channel their current civic

Factorization meets the neighborhood: a multifaceted collaborative filtering model

Abstract: Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent factor models, which directly profile both users and products, and neighborhood models, which analyze similarities between products or users. In this work we introduce some innovations to both approaches. The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Netflix data. Results are better than those previously published on that dataset. In addition, we suggest a new evaluation metric, which highlights the differences among methods, based on their performance at a top-K recommendation task.