On Learning Prediction Models for Tourists Paths
| Autor: | Ranieri Baraglia, Fabrizio Silvestri, Cristina Ioana Muntean, Franco Maria Nardini |
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| Rok vydání: | 2023 |
| Předmět: |
Computer science
business.industry Feature vector Supervised learning Geographical PoI prediction Learning to rank Machine learning computer.software_genre Popularity Theoretical Computer Science Artificial Intelligence Robustness (computer science) Ranking SVM Data mining Artificial intelligence Baseline (configuration management) business computer Predictive modelling |
| Zdroj: | ACM transactions on intelligent systems and technology 7 (2015): 8–35. doi:10.1145/2766459 info:cnr-pdr/source/autori:Muntean C.I.; Nardini F.M.; Silvestri F.; Baraglia R./titolo:On learning prediction models for tourists paths/doi:10.1145%2F2766459/rivista:ACM transactions on intelligent systems and technology (Print)/anno:2015/pagina_da:8/pagina_a:35/intervallo_pagine:8–35/volume:7 |
| DOI: | 10.1145/2766459 |
| Popis: | In this article, we tackle the problem of predicting the “next” geographical position of a tourist, given her history (i.e., the prediction is done accordingly to the tourist’s current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Ranking SVM. The learning is done on the basis of an object space represented by a 68-dimension feature vector specifically designed for tourism-related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-the-art in recommender and trail prediction systems for tourism, as well as a popularity baseline. Experiments show that the methods we propose consistently outperform the baselines and provide strong evidence of the performance and robustness of our solutions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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