CURUMIM: A Serendipitous Recommender System for Tourism Based on Human Curiosity
| Autor: | Rebeca Ferreira, Laura Sebastia, Alan Menk |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Social network
business.industry Computer science Serendipity media_common.quotation_subject 05 social sciences Novelty 02 engineering and technology Recommender system Space (commercial competition) World Wide Web Surprise 0502 economics and business 0202 electrical engineering electronic engineering information engineering Curiosity 020201 artificial intelligence & image processing business 050203 business & management Tourism media_common |
| Zdroj: | ICTAI |
| DOI: | 10.1109/ictai.2017.00124 |
| Popis: | In recent years, an evolution of recommendation systems has been observed. New advisory systems have been gaining space, using new tools, algorithms and recommending techniques, not only to increase accuracy, but also to positively surprise the user, that is, to provide serendipitous recommendations. Following this trend, we describe CURUMIM, an online tourism recommender system, able to generate serendipitous recommendations of places around the world. In summary, from data available on social networks, it predicts the degree of curiosity of a user, which is then used, along with the user history and her level of education, to select the most appropriate recommendations. We have performed an experiment with real users who reported positive levels of satisfaction with the recommendations in terms of accuracy, serendipity and novelty. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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