Recommender Systems: Kembellec/Recommender Systems

By Gérald Kembellec, G. Chartron, Imad Saleh. 2014

1,599 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

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

In Artificial Intelligence

1. Tao Zhou
2. Linyuan Lu
3. Yi-Cheng Zhang
4. Francesco Ricci
5. Jie Lu
6. Mahdi Jalili
7. Lior Rokach
8. Scott Sanner

In Information Systems

1. Francesco Ricci
2. Muhammad Ali Imran
3. Jie Lu
4. Yi-Cheng Zhang
5. Mahdi Jalili
6. Linyuan Lu
7. Lior Rokach
8. Scott Sanner

Citations in the corpus

• Efficient Private Statistics with Succinct Sketches (2015, neutral): “Recommender systems are used to predict the utility of a certain item for a particular user, based on their previous ratings as well as those of other “similar” users [58].”
• A probabilistic model to resolve diversity–accuracy challenge of recommendation systems (2015, background): “Due to this reason, recommender systems have become a core component for many e-commerce applications and attracted many researches in recent years [1-3].”
• Interactive Visual Profiling of Musicians (2016, methodology): “Basically, the initial step of our profiling approach imitates recommendation based on item similarity [40].”
• A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks (2017, background): “6) Recommender Systems: Also known as Collaborative Filtering (CF) [75], [76], are a class of algorithms with the objective to provide suggestions for users based on the”
• Data Mining for the Internet of Things: Literature Review and Challenges (2015, methodology): “A complementary method of identifying potentially interesting content uses data on the preference of a set of users, called collaborative filtering or recommender systems [97–99], and it leverages user’s correlation and other similarity metrics to identify and cluster similar user profiles for the p”
• Bayesian tuning and bandits : an extensible, open source library for AutoML (2018, background): “The highest level of search is recommendation [25].”
• Dynamics of information diffusion and its applications on complex networks (2016, background): “However, the information diffusion is quite a dynamical and timely process (see the previous article [299]), classical recommender systemswould fail to provide such real-time service in a detailedmicro-level.”
• Collaborative filtering recommender systems (2013, methodology): “Recommendation methods have a variety of possible categories (Resnick and Varian, 1997; Schafer et al ., 1999).”