Conjugate-Prior-Regularized Multinomial pLSA for Collaborative Filtering
| Autor: | Stefan Ingi Adalbjörnsson, Johan Sward, Marcus Klasson, Soren Vang Andersen |
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| Rok vydání: | 2018 |
| Předmět: |
Probabilistic latent semantic analysis
business.industry Computer science 02 engineering and technology Recommender system Machine learning computer.software_genre Conjugate prior 020204 information systems 0202 electrical engineering electronic engineering information engineering Collaborative filtering Maximum a posteriori estimation 020201 artificial intelligence & image processing Multinomial distribution Artificial intelligence business Heuristics computer |
| Zdroj: | EUSIPCO |
| DOI: | 10.5281/zenodo.1159322 |
| Popis: | We consider the over-fitting problem for multinomial probabilistic Latent Semantic Analysis (pLSA) in collaborative filtering, using a regularization approach. For big data applications, the computational complexity is at a premium and we, therefore, consider a maximum a posteriori approach based on conjugate priors that ensure that complexity of each step remains the same as compared to the un-regularized method. In the numerical section, we show that the proposed regularization method and training scheme yields an improvement on commonly used data sets, as compared to previously proposed heuristics. |
| Databáze: | OpenAIRE |
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