A Genetic Algorithm for Travel Itinerary Recommendation with Mandatory Points-of-Interest
| Autor: | Phatpicha Yochum, Zhu Manli, Chen Hongliang, Tianlong Gu, Liang Chang |
|---|---|
| Přispěvatelé: | Guilin University of Electronic Technology, Zhongzhi Shi, Sunil Vadera, Elizabeth Chang, TC 12 |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Profit (real property)
Information retrieval Point of interest Computer science Itinerary recommendations Travel recommendations Location recommendations 02 engineering and technology Recommender system Popularity Recommendation systems Term (time) Travel time 020204 information systems Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Duration (project management) |
| Zdroj: | IFIP Advances in Information and Communication Technology 11th International Conference on Intelligent Information Processing (IIP) 11th International Conference on Intelligent Information Processing (IIP), Jul 2020, Hangzhou, China. pp.133-145, ⟨10.1007/978-3-030-46931-3_13⟩ IFIP Advances in Information and Communication Technology ISBN: 9783030469306 Intelligent Information Processing |
| Popis: | Part 3: Recommendation System; International audience; Traveling as a very popular leisure activity enjoyed by many people all over the world. Typically, people would visit the POIs that are popular or special in a city and also have desired starting POIs (e.g., POIs that are close to their hotels) and destination POIs (e.g., POIs that are near train stations or airports). However, travelers often have limited travel time and are also unfamiliar with the wide range of Points-of-Interest (POIs) in a city, so that the itinerary planning is time-consuming and challenging. In this paper, we view this kind of itinerary planning as MandatoryTour problem, which is tourists have to construct an itinerary comprising a series of POIs of a city and including as many popular or special POIs as possible within their travel time budget. We term the most popular and special POIs as mandatory POIs in our paper. For solving the presented MandatoryTour problem, we propose a genetic algorithm GAM. We compare our approach against several baselines GA, MaxM, and GreedyM by using real-world datasets from the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), which include POI visits of seven touristic cities. The experimental results show that GAM achieves better recommendation performance in terms of the mandatory POIs, POIs visited, time budget (travel time and visit duration), and profit (POI popularity). |
| Databáze: | OpenAIRE |
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