One-class collaborative filtering with random graphs

By Ulrich Paquet, Noam Koenigstein. 2013

111 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Graph Learning, Recommender Systems, Signal Processing

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. David M. Blei
2. Andriy Mnih
3. Yoshua Bengio
4. Shakir Mohamed
5. Mihaela Rosca
6. Prem Gopalan
7. Julian McAuley
8. Oren Barkan

In Information Systems

1. David M. Blei
2. Julian McAuley
3. Prem Gopalan
4. Oren Barkan
5. Jake M. Hofman
6. Zhong Ming
7. Weike Pan
8. Shay Ben-Elazar

In Graph Learning

1. Oren Barkan
2. David M. Blei
3. Julian McAuley
4. Prem Gopalan
5. Jake M. Hofman
6. Shay Ben-Elazar
7. Noam Koenigstein
8. Shakir Mohamed

In Recommender Systems

1. David M. Blei
2. Prem Gopalan
3. Oren Barkan
4. Julian McAuley
5. Jake M. Hofman
6. Irwin King
7. Weike Pan
8. Noam Koenigstein

In Signal Processing

1. David M. Blei
2. Oren Barkan
3. Prem Gopalan
4. Jake M. Hofman
5. Noam Koenigstein
6. Andriy Mnih
7. Irwin King
8. Shay Ben-Elazar

Citations in the corpus

• Bayesian Neural Word Embedding (2016, methodology): “(3) as follows These biases often explain properties such as frequency of a word in the text [1], [3] or popularity of an item in the dataset [11].”
• Scalable Recommendation with Poisson Factorization (2013, methodology): “Variational inference tends to scale better than alternative samplingbased approaches, like Monte Carlo Markov chain sampling, and has been deployed to solve many applied problems with complex models, including large-scale recommendation [27].”
• ITEM2VEC: Neural item embedding for collaborative filtering (2016, methodology): “In future we plan to investigate more complex CF models such as [1, 2, 3] and compare between them and item2vec.”
• Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces (2014, background): “A Bayesian MF model is also at the heart of the Xbox recommendation system [16] which serves games, movies, and music recommendations to millions of users daily.”
• Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering (2014, background): “One-class Collaborative Filtering Several works have studied one-class collaborative filtering and can be mainly divided into two branches: pointwise methods [5, 12] and pairwise methods [14, 15, 16, 18, 22].”
• A Hybrid Explanations Framework for Collaborative Filtering Recommender Systems (2014, methodology): “RecSys 2014 Poster Proceedings, October 6 - 10, 2014, Foster City, Silicon Valley, USA. system [2, 4], recommendations are based on a global optimization process and there is no clear method to associate an explanation item with the recommended item.”
• Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks (2015, methodology): “The BPR algorithm is a seminal work for homogeneous implicit feedbacks, which has been empirically proved to be very effective [23].”
• Scalable Recommendation with Hierarchical Poisson Factorization (2015, methodology): “When modeling implicit feedback data sets, researchers have proposed merging factorization techniques with neighborhood models [22], weighting techniques to adjust the relative importance of positive examples [17], and sampling-based approaches to create informative negative examples [8, 7, 29].”
• Monte Carlo Gradient Estimation in Machine Learning (2019, methodology): “Stochastic variational message passing (Winn and Bishop, 2005; Paquet and Koenigstein, 2013) uses mean-field approximations, bounding methods, and natural parameterisations of the distributions involved, to create another type of stochastic computational graph that uses more detailed information abo”