Curumim: A Serendipitous Recommender System based on Human Curiosity
| Autor: | Rebeca Ferreira, Laura Sebastia, Alan Menk |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Social network
Computer science business.industry Serendipity media_common.quotation_subject Novelty 020207 software engineering 02 engineering and technology Recommender system Surprise Service (economics) 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences Curiosity Personality 020201 artificial intelligence & image processing Artificial intelligence Marketing business Tourism General Environmental Science media_common |
| Zdroj: | KES |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2017.08.098 |
| Popis: | Tourism is an important source of income of countries, since nowadays 10% of GDP corresponds to a direct, indirect or induced effect of tourism. In European countries like Spain, the activity reached numbers like 11% of GDP in 2016. Therefore, providing an efficient and personalised service for tourists has become an essential issue in the development of new technological resources. This work aims to build a better experience for the tourist through the fusion of three axes: human psychology, namely curiosity, technological innovation and social networks. This article describes CURUMIM system, which, from data available on social networks, predicts the level of curiosity of a user and then, tied to other measures, generates novel and serendipitous recommendations of touristic places around the world. In other words, the recommendations will be accurate and adapted to the level of curiosity of a given user on one hand and, on the other, they will positively surprise the users. |
| Databáze: | OpenAIRE |
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