Proceedings ArticleDOI
Beyond accuracy: evaluating recommender systems by coverage and serendipity
Mouzhi Ge,Carla Delgado-Battenfeld,Dietmar Jannach +2 more
- pp 257-260
TLDR
It is argued that the new ways of measuring coverage and serendipity reflect the quality impression perceived by the user in a better way than previous metrics thus leading to enhanced user satisfaction.Abstract:
When we evaluate the quality of recommender systems (RS), most approaches only focus on the predictive accuracy of these systems. Recent works suggest that beyond accuracy there is a variety of other metrics that should be considered when evaluating a RS. In this paper we focus on two crucial metrics in RS evaluation: coverage and serendipity. Based on a literature review, we first discuss both measurement methods as well as the trade-off between good coverage and serendipity. We then analyze the role of coverage and serendipity as indicators of recommendation quality, present novel ways of how they can be measured and discuss how to interpret the obtained measurements. Overall, we argue that our new ways of measuring these concepts reflect the quality impression perceived by the user in a better way than previous metrics thus leading to enhanced user satisfaction.read more
Citations
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Journal ArticleDOI
Research-paper recommender systems: a literature survey
TL;DR: Several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.
Journal ArticleDOI
Deep Learning based Recommender System: A Survey and New Perspectives.
Shuai Zhang,Lina Yao,Aixin Sun +2 more
TL;DR: A taxonomy of deep learning-based recommendation models is provided and a comprehensive summary of the state of the art is provided, along with new perspectives pertaining to this new and exciting development of the field.
Journal ArticleDOI
Internet Research in Psychology
Samuel D. Gosling,Winter Mason +1 more
TL;DR: An overview of the literature is provided, considering three broad domains of research: translational (implementing traditional methods online), phenomenological (topics spawned or mediated by the Internet; e.g., cyberbullying), and novel (new ways to study existing topics; eg., rumors).
Journal ArticleDOI
Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems
Marius Kaminskas,Derek Bridge +1 more
TL;DR: A survey of the most discussed beyond-accuracy objectives in recommender systems research: diversity, serendipity, novelty, and coverage is presented and the positive influence of novelty on recommendation coverage is demonstrated.
Book ChapterDOI
Novelty and Diversity in Recommender Systems
TL;DR: An overview of the main contributions to this area in the field of recommender systems, and seeks to relate them together in a unified view, analyzing the common elements underneath the different forms under which novelty and diversity have been addressed, and identifying connections to closely related work on diversity in other fields.
References
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Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
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.
Book ChapterDOI
Evaluating Recommendation Systems
Guy Shani,Asela Gunawardana +1 more
TL;DR: This paper discusses how to compare recommenders based on a set of properties that are relevant for the application, and focuses on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms.