Beyond accuracy: evaluating recommender systems by coverage and serendipity

By Mouzhi Ge, Carla Amor Divino Moreira Delgado, Dietmar Jannach. ACM Conference on Recommender Systems, 2010

761 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/16c029e8f4bc3b662b0ad89d15dd57ff567f3726

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yi Tay
2. Michael Jugovac
3. Mozhgan Karimi
4. Bela Gipp
5. Shuai Zhang
6. Marius Kaminskas
7. Dietmar Jannach
8. Maurizio Morisio

In Information Systems

1. Yi Tay
2. Aixin Sun
3. Lina Yao
4. Shuai Zhang
5. Michael Jugovac
6. Mozhgan Karimi
7. Bela Gipp
8. Marius Kaminskas

In Management Science and Operations Research

1. Yi Tay
2. Michael Jugovac
3. Mozhgan Karimi
4. Aixin Sun
5. Shuai Zhang
6. Lina Yao
7. Bela Gipp
8. Marius Kaminskas

Citations in the corpus

• Hybrid recommender systems: A systematic literature review (2017, background): “Diversity This is a desired characteristic that is getting attention recently [23].”
• Diversity, Serendipity, Novelty, and Coverage (2016, background): “Since the latter formulation is more commonly found in the RS literature [Herlocker et al. 2004; Ge et al. 2010; Adomavicius and Kwon 2012] and to be consistent with the other discussed beyondaccuracy objectives (which are item properties rather than user properties), in this work we focus on "…”
• A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation (2013, background): “In addition, some arguments have been voiced that offline-evaluations are not adequate to evaluate recommender systems [6,9,17].”
• Recommender Systems (2016, neutral): “Metrics for serendipity evaluation are discussed in [214, 450].”
• Research-paper recommender systems: a literature survey (2015, background): “It is commonly assumed that the objective of a recommender system is to make users “happy” [226] by satisfying their”
• Deep Learning Based Recommender System (2017, background): “ven lead to over-specialization. Apart from accuracy, other evaluation metrics such as, diversity [3, 102], novelty, serendipity, coverage, trustworthiness, privacy, interpretability etc. also ma−ers [29, 50, 76, 110]. For example, recommender system will bring customers with unclear and uncertain i”
• News recommender systems - Survey and roads ahead (2018, methodology): “[57], based on an earlier approach by Murakami et al.”