Recommender Systems Handbook

By Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. Kantor. Recommender Systems Handbook, 2010

535 citations recorded by the source corpus.

Subfields: Information Systems, Management of Technology and Innovation

View Paper: https://www.semanticscholar.org/paper/d25927f9a82ca68ad5d6b6f1bcaefbd1d9963e82

How researchers see this paper

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

In Information Systems

1. Nava Tintarev
2. Judith Masthoff
3. Tommaso Di Noia
4. Markus Zanker
5. Vito Claudio Ostuni
6. Yongfeng Zhang
7. Dietmar Jannach
8. Longo Luca

Citations in the corpus

• Evaluating the effectiveness of explanations for recommender systems (2012, background): “While recommender systems have traditionally been evaluated in terms of recommendation accuracy, in recent years, interest has increased in more user-centered evaluation metrics such as user satisfaction (e.g. McNee et al. 2006a; Tintarev and Masthoff 2007b, 2010; Ricci et al. 2010).”
• Linked open data to support content-based recommender systems (2012, background): “Among them, we mention a specific family of such systems for information filtering: recommender systems [20], whose objective is supporting users to make choices.”
• Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews (2017, unknown): “Finally, we also calculate the rating prediction performance of the SULM model using the standard performance measures Precision@Top3 and AUC [23].”