Proceedings ArticleDOI
A Survey of Explanations in Recommender Systems
Nava Tintarev,Judith Masthoff +1 more
- pp 801-810
TLDR
This paper provides a comprehensive review of explanations in recommender systems, highlighting seven possible advantages of an explanation facility, and describing how existing measures can be used to evaluate the quality of explanations.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.read more
Citations
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Proceedings ArticleDOI
Factorization meets the neighborhood: a multifaceted collaborative filtering model
TL;DR: The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model and a new evaluation metric is suggested, which highlights the differences among methods, based on their performance at a top-K recommendation task.
Journal ArticleDOI
Recommender systems survey
TL;DR: An overview of recommender systems as well as collaborative filtering methods and algorithms is provided, which explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.
Journal ArticleDOI
Ontology Matching: State of the Art and Future Challenges
Pavel Shvaiko,Jérôme Euzenat +1 more
TL;DR: It is conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching and presents such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field.
Book ChapterDOI
Advances in Collaborative Filtering
Yehuda Koren,Robert M. Bell +1 more
TL;DR: In this paper, the authors survey the recent progress in the field of collaborative filtering and describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field and demonstrate how to utilize temporal models and implicit feedback to extend models accuracy.
Journal ArticleDOI
Factor in the neighbors: Scalable and accurate collaborative filtering
TL;DR: A new neighborhood model with an improved prediction accuracy is introduced, which model neighborhood relations by minimizing a global cost function and makes both item-item and user-user implementations scale linearly with the size of the data.
References
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Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Proceedings ArticleDOI
Heuristic evaluation of user interfaces
Jakob Nielsen,Rolf Molich +1 more
TL;DR: Four experiments showed that individual evaluators were mostly quite bad at doing heuristic evaluations and that they only found between 20 and 51% of the usability problems in the interfaces they evaluated.
Journal ArticleDOI
Construction and Validation of a Scale to Measure Celebrity Endorsers' Perceived Expertise, Trustworthiness, and Attractiveness
TL;DR: The authors developed a 15-item semantic differential scale to measure perceived expertise, trustworthiness, and attractiveness of celebrity endorsers, which was validated using respondents' self-reported measures of intention to purchase and perception of quality for the products being tested.
Proceedings ArticleDOI
Improving recommendation lists through topic diversification
TL;DR: This work presents topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests, and introduces the intra-list similarity metric to assess the topical diversity of recommendation lists.
Proceedings ArticleDOI
Explaining collaborative filtering recommendations
TL;DR: This paper presents experimental evidence that shows that providing explanations can improve the acceptance of ACF systems, and presents a model for explanations based on the user's conceptual model of the recommendation process.