Collaborative Filtering with Recurrent Neural Networks
By Robin Devooght, Hugues Bersini. 2016
90 citations recorded by the source corpus.
We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation. We show that the LSTM is competitive in all aspects, and largely outperforms other methods in terms of item coverage and short term predictions.
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
- Alexandros Karatzoglou
- Paolo Cremonesi
- Balázs Hidasi
- Massimo Quadrana
- Kun Gai
- Dietmar Jannach
- Jie He
- Han Zhu
In Information Systems
- Alexandros Karatzoglou
- Massimo Quadrana
- Paolo Cremonesi
- Balázs Hidasi
- Dietmar Jannach
- Kun Gai
- Jie He
- Han Zhu
Citations in the corpus
- Recurrent Neural Networks with Top-k Gains for Session-based Recommendations (2017, methodology): “RNNs have also been used in more standard user-item collaborative filtering settings where the aim is to model the evolution of the user and items factors [21],[6] where the results are less striking, with the proposed methods barely outperforming standard matrix factorization methods.”
- Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks (2017, methodology): “RNN’s have also been used in more standard user-item collaborative filtering settings where the aim is to model the evolution of the user and items factors [20] [4] where the results are though less impressive, with the proposed methods barely outperforming standard matrix factorization methods.”
- Deep Learning Based Recommender System (2017, unknown): “RNNs [5, 28, 35, 56, 57, 73, 78, 90, 118, 133, 140, 143, 175–177], [24, 29, 33, 55, 68, 91, 108, 113, 134, 142, 150, 174, 180] RBM [42, 71, 72, 101, 124, 168, 181] NADE [36, 204, 205]”
- Learning Tree-based Deep Model for Recommender Systems (2018, methodology): “ainsuser-movieratingswithtimestamps in this dataset. As we deal with implicit feedback problem,the ratings are binarized by keeping the ratings of four or higher, which is a common way in other works [7, 16]. Besides, only the users who have watched at least 10 movies are kept. To create training, v”