Including the Temporal Dimension in the Generation of Personalized Itinerary Recommendations

Abstract

Tourists spend a lot of effort in planning itineraries when organizing a trip. This is a complex activity that involves selecting the places to visit and dealing with a number of temporal issues to generate a schedule for the visit. In the paper we propose an architecture for a recommender that suggests personalized tourist itineraries and a personalized time schedule. The approach takes into account (i) user preferences for the places to be included in the itinerary and (ii) several temporal dimensions concerning both temporal information and constraints (e.g., opening hours, time for visiting each place, time to move among places) and time-related user preferences (e.g., number of days of the visit, preferring a dense schedule vs having a lot of free time, the variety of the types of attractions during a day of visit). The approach is based on a combination of genetic algorithms and temporal reasoning. It focuses on generating temporally annotated itineraries starting from a ranked list of places to be included. Thus, our solution is designed as a module that can be coupled with any system recommending places to visit. The design started from a user study we carried out to analyze the temporal dimensions to take into account, and to find relationships between such dimensions and users’ personality traits, and led to the development and evaluation of a prototypical implementation that generates personalized itineraries for the city of Turin.

Keywords

• Itinerary recommendation
• recommender system
• temporal information