Long and Short-Term Recommendations with Recurrent Neural Networks

By Robin Devooght, Hugues Bersini. 2017

133 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

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. Malte Ludewig
2. Dietmar Jannach
3. Ali Yurekli
4. Cihan Kaleli
5. Alper Bilge
6. Zeynep Batmaz
7. Aminu Da’u
8. Naomie Binti Salim

In Information Systems

1. Malte Ludewig
2. Dietmar Jannach
3. Ali Yurekli
4. Cihan Kaleli
5. Alper Bilge
6. Zeynep Batmaz
7. Aminu Da’u
8. Naomie Binti Salim

In Management Science and Operations Research

1. Malte Ludewig
2. Dietmar Jannach
3. Ali Yurekli
4. Cihan Kaleli
5. Alper Bilge
6. Zeynep Batmaz
7. Aminu Da’u
8. Naomie Binti Salim

Citations in the corpus

• Evaluation of session-based recommendation algorithms (2018, methodology): “Since these features are domain specific and such features are only available for very few of our datasets, we limit ourselves to methods that do not rely on such types of data in this paper.”
• A review on deep learning for recommender systems: challenges and remedies (2018, methodology): “(2015) achieve higher accuracy by predicting missing ratings of a user-item matrix with the help of autoencoders, and Devooght and Bersini (2017) utilize neural networks to improve short-term prediction accuracy by converting CF into a sequence prediction problem. Wang et al. (2015b) propose a deep ”