An approach to hybrid personalized recommender systems
| Autor: | Mehmet S. Aktas, Zafer Duzen |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry 010401 analytical chemistry Usability 02 engineering and technology Music listening Recommender system Machine learning computer.software_genre 01 natural sciences 0104 chemical sciences Prediction algorithms 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer |
| Zdroj: | INISTA |
| Popis: | Collaborating Filtering (CF) is a recommendation method that can make predictions about a given user's interest by collecting a large number of other user' appreciation. Some of the major problems encountered in the use of CF are the cold-start problem and the fact that personalized recommendations cannot be done. In turn, CF-based recommendations produces ranked results where the success rate can be improved. The method proposed in this research is a hybrid recommender system that utilizes Case-Based Reasoning (CBR) in order to overcome these shortcomings and improve the success rate of the recommender system. To show the usability of the proposed hybrid recommender method, we have used a music recommendation dataset and build music listening assistant that uses the implementation of the method. The performance of the proposed method was evaluated and results are reported. The results indicate that our proposed method is successful. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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