Matrix Factorization Based Collaborative Filtering With Resilient Stochastic Gradient Descent
| Autor: | Islam Elnabarawy, Khalid M. Salama, Ashraf M. Abdelbar, Donald C. Wunsch |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
Stochastic process Computer science MathematicsofComputing_NUMERICALANALYSIS 02 engineering and technology MovieLens Matrix decomposition Matrix (mathematics) Stochastic gradient descent Factorization 020204 information systems Test set 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing Gradient descent Cluster analysis Algorithm |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn.2018.8489528 |
| Popis: | One of the leading approaches to collaborative filtering is to use matrix factorization to discover a set of latent factors that explain the pattern of preferences. In this paper, we apply a resilient stochastic gradient descent approach that uses only the sign of the gradient, similar to the R-Prop algorithm in neural network training, to matrix factorization for collaborative filtering. We evaluate the performance of our approach on the MovieLens 1M dataset, and find that test set accuracy markedly improves compared to standard gradient descent. As a follow-up experiment, we apply clustering to the learned item-factor matrix in factor space, and attempt to manually characterize each cluster of movies. |
| Databáze: | OpenAIRE |
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