Conjugate priors for Gaussian emission plsa recommender systems
| Autor: | Andreas Jakobsson, Soren Vang Andersen, Stefan Ingi Adalbjörnsson, Johan Sward, Magnus Orn Berg |
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| Rok vydání: | 2016 |
| Předmět: |
Probabilistic latent semantic analysis
business.industry Latent semantic analysis Computer science Probabilistic logic 02 engineering and technology Recommender system computer.software_genre Machine learning Conjugate prior MovieLens 020204 information systems Prior probability 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing Artificial intelligence Data mining business computer |
| Zdroj: | EUSIPCO |
| DOI: | 10.1109/eusipco.2016.7760618 |
| Popis: | Collaborative filtering for recommender systems seeks to learn and predict user preferences for a collection of items by identifying similarities between users on the basis of their past interest or interaction with the items in question. In this work, we present a conjugate prior regularized extension of Hofmann's Gaussian emission probabilistic latent semantic analysis model, able to overcome the over-fitting problem restricting the performance of the earlier formulation. Furthermore, in experiments using the EachMovie and MovieLens data sets, it is shown that the proposed regularized model achieves significantly improved prediction accuracy of user preferences as compared to the latent semantic analysis model without priors. |
| Databáze: | OpenAIRE |
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