Personalized Itinerary Recommendation with Queuing Time Awareness

Abstract

Personalized itinerary recommendation is a complex and time-consuming problem, due to the need to recommend popular attractions that are aligned to the interest preferences of a tourist, and to plan these attraction visits as an itinerary that has to be completed within a specific time limit. Furthermore, many existing itinerary recommendation systems do not automatically determine and consider queuing times at attractions in the recommended itinerary, which varies based on the time of visit to the attraction, e.g., longer queuing times at peak hours. To solve these challenges, we propose the PersQ algorithm for recommending personalized itineraries that take into consideration attraction popularity, user interests and queuing times. We also implement a framework that utilizes geo-tagged photos to derive attraction popularity, user interests and queuing times, which PersQ uses to recommend personalized and queue-aware itineraries. We demonstrate the effectiveness of PersQ in the context of five major theme parks, based on a Flickr dataset spanning nine years. Experimental results show that PersQ outperforms various state-of-the-art baselines, in terms of various queuing-time related metrics, itinerary popularity, user interest alignment, recall, precision and F1-score.