408,914 research outputs found
A Puzzle About Economic Explanation: Examining the Cournot and Bertrand Models of Duopoly Competition
Economists use various models to explain why it is that firms are capable of pricing above marginal cost. In this paper, we will examine two of them: the Cournot and Bertrand duopoly models. Economists generally accept both models as good explanations of the phenomenon, but the two models contradict each other in various important ways. The puzzle is that two inconsistent explanations are both regarded as good explanations for the same phenomenon. This becomes especially worrisome when the two models are offering divergent policy recommendations. This report presents that puzzle by laying out how the two models contradict each other in a myriad of ways and then offers five possible solutions to that puzzle from various economists, philosophers of science, and philosophers of economics
{ELIXIR}: {L}earning from User Feedback on Explanations to Improve Recommender Models
System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback
Make it personal: a social explanation system applied to group recommendations
Recommender systems help users to identify which items from a variety of choices best match their needs and preferences. In this context, explanations act as complementary information that can help users to better comprehend the systemâs output and to encourage goals such as trust, confidence in decision-making or utility. In this paper we propose a Personalized Social Individual Explanation approach (PSIE). Unlike other expert systems the PSIE proposal novelly includes explanations about the systemâs group recommendation and explanations about the groupâs social reality with the goal of inducing a positive reaction that leads to a better perception of the received group recommendations. Among other challenges, we uncover a special need to focus on âtactfulâ explanations when addressing usersâ personal relationships within a group and to focus on personalized reassuring explanations that encourage users to accept the presented recommendations. Besides, the resulting intelligent system significatively increases usersâ intent (likelihood) to follow the recommendations, usersâ satisfaction and the systemâs efficiency and trustworthiness
Mental health inpatient services: Improving our understanding of the needs of Maori when acutely unwell
There are many possible explanations for the pattern of Maori overrepresentation in mental health acute services. This research project focuses on how services can optimally meet the needs of Maori to improve outcomes. This doctoral research in progress is about claiming space for Maori to have a voice in identifying factors that contribute to recovery and Whanau Ora, and offering recommendations to improve the effectiveness of existing services to better meet the needs of Maori Tangata Whaiora and Whanau
Content-Based Book Recommending Using Learning for Text Categorization
Recommender systems improve access to relevant products and information by
making personalized suggestions based on previous examples of a user's likes
and dislikes. Most existing recommender systems use social filtering methods
that base recommendations on other users' preferences. By contrast,
content-based methods use information about an item itself to make suggestions.
This approach has the advantage of being able to recommended previously unrated
items to users with unique interests and to provide explanations for its
recommendations. We describe a content-based book recommending system that
utilizes information extraction and a machine-learning algorithm for text
categorization. Initial experimental results demonstrate that this approach can
produce accurate recommendations.Comment: 8 pages, 3 figures, Submission to Fourth ACM Conference on Digital
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