Abstract
We introduce various data-driven models for recommending both city destinations and within-city points of interest to tourists. The models are implemented with a novel dataset of travel histories, derived from social media data, which is larger by size and scope than in prior work. All proposed models outperform simple baselines in cross-validation experiments, with the strongest variants reliably including tourists' true movements among their top recommendations.
| Original language | English |
|---|---|
| Pages | 51-56 |
| Publication status | Published - 21 Nov 2019 |
| Event | International Alan Turing Conference on Decision Support and Recommender Systems - The Alan Turing Institute, London, United Kingdom Duration: 21 Nov 2019 → 22 Nov 2019 Conference number: 1 |
Conference
| Conference | International Alan Turing Conference on Decision Support and Recommender Systems |
|---|---|
| Abbreviated title | DSRS-Turing '19 |
| Country/Territory | United Kingdom |
| City | London |
| Period | 21/11/19 → 22/11/19 |
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