Temporal dynamic study in personalization digital newspaper ¡ahora!
| Autor: | Angel Luis Scull Pupo, Luis Angel Hernandez Leyva, Leandro William Osorio Gamez, Sergio Cleger Tamayo |
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| Rok vydání: | 2015 |
| Předmět: |
General Computer Science
business.industry Computer science Principal (computer security) Recommender system Machine learning computer.software_genre Field (computer science) Personalization Dynamics (music) Face (geometry) Collaborative filtering Artificial intelligence Electrical and Electronic Engineering business Hidden Markov model computer |
| Zdroj: | IEEE Latin America Transactions. 13:2792-2797 |
| ISSN: | 1548-0992 |
| Popis: | In recent years the utilization of Recommender Systems has greatly increased, and with it the investigations in this area. Each time investigators strive harder to find techniques and tools that allow improving the performance of said systems. One of the principal problems that investigators face in this field is the continuous change of the users' preferences throughout time, whose analysis supposes an approach to the tastes and preferences of the users. In the present investigation the objective is the design of a model for Recommender Systems in collaborative filtering with temporary dynamics. The proposed model is developed with the utilization of a Hidden Markov Model. This technique is employed with the goal of tracking the continuous change in the users' preferences in time. The proposed solution is described as well as the experimentation carried out to validate the model. The obtained results show a better performance of the proposed model that incorporates the temporary dynamics on the base model that does not have this in mind. |
| Databáze: | OpenAIRE |
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