Offline and online evaluation of news recommender systems at swissinfo.ch

By Florent Garcin, Boi Faltings, Olivier Donatsch, A. Alazzawi, Christophe Bruttin, A. Huber. 2014

193 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Sociology and Political Science, Evaluation & Benchmarks, Recommender Systems

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. Dietmar Jannach
2. George Tucker
3. Justin Fu
4. Aviral Kumar
5. Li Chen
6. Paolo Cremonesi
7. Stuart J. Russell
8. Gediminas Adomavicius

In Information Systems

1. Dietmar Jannach
2. Paolo Cremonesi
3. Li Chen
4. Gediminas Adomavicius
5. Massimo Quadrana
6. Robin D. Burke
7. Maurizio Ferrari Dacrema
8. Himan Abdollahpouri

In Sociology and Political Science

1. Dietmar Jannach
2. George Tucker
3. Li Chen
4. Sergey Levine
5. Gediminas Adomavicius
6. Robin D. Burke
7. Toshihiro Kamishima
8. Indrė Žliobaitė

In Evaluation & Benchmarks

1. Sergey Levine
2. Aviral Kumar
3. George Tucker
4. Stuart J. Russell
5. Dietmar Jannach
6. Gediminas Adomavicius
7. Justin Fu
8. Jiantao Jiao

In Recommender Systems

1. Li Chen
2. Dietmar Jannach
3. George Tucker
4. Aviral Kumar
5. Paolo Cremonesi
6. Sergey Levine
7. Gediminas Adomavicius
8. Robin D. Burke

Citations in the corpus

• Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems (2020, background): “Policy learning from logged, offline data has included studies on optimizing newspaper article click-through-rates (Strehl et al., 2010; Garcin et al., 2014), advertisement ranking on search pages (Bottou et al., 2013), and personalized ad recommendation for digital marketing (Theocharous et al.,…”
• Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism (2021, background): “This paradigm is useful in a diverse array of application domains such as healthcare [51, 15, 35], autonomous driving [57, 4, 36], and recommendation systems [47, 12, 49].”
• A Survey on Conversational Recommender Systems (2020, background): “e mentioned papers, shorter interaction or task completion times are generally considered favorable. Note however, that in some cases longer sessions are desirable. In particular, longer 13See, e.g., [10,35,43,84,122]. A Survey on Conversational Recommender Systems 1:27 interaction times might refle”
• Are we really making much progress? A worrying analysis of recent neural recommendation approaches (2019, background): “In fact, a number of research works exist that indicate that higher accuracy does not necessarily translate into betterreceived recommendations [4, 9, 13, 31, 37].”
• Sequence-Aware Recommender Systems (2018, background): “The few papers that exist include [33], [37], [62], [84] and [97].”
• Multistakeholder recommendation: Survey and research directions (2020, background): “, in terms of increased sales or click-through rates (Garcin et al. 2014; Kirshenbaum et al. 2012; Jannach and Hegelich 2009); and, viceversa, that unexpected or irrelevant recommendations can lead to a decreased quality perception and trust by consumers (Chau et al.”