+Tour: Recommending personalized itineraries for smart tourism

TL;DR

This work tackles the problem of jointly optimizing personalized tour itineraries and MEC resource allocation for advanced mobile tourism applications. It introduces +Tour, a two-stage optimization that first generates Pareto-efficient itineraries using an ESPPRC-based DP approach and then solves a MILP to allocate MEC resources and balance physical user profit with edge resource utilization. The approach is grounded in a Flickr-derived, 13-city dataset that characterizes POI popularity, visiting times, and user category interests, and is evaluated against a resource-aware PersTour baseline across multiple network overload scenarios. Results show substantial improvements in Allocation Efficiency and User Experience (up to 11% and 40%, respectively) while preserving competitive traditional PTIR metrics, demonstrating the practical value of edge-aware personalized tourism. The work also provides open data and code, and points to future enhancements like graph neural networks to further capture travel patterns and integrate broader immersive services such as metaverse-type experiences.

Abstract

Next-generation touristic services will rely on the advanced mobile networks' high bandwidth and low latency and the Multi-access Edge Computing (MEC) paradigm to provide fully immersive mobile experiences. As an integral part of travel planning systems, recommendation algorithms devise personalized tour itineraries for individual users considering the popularity of a city's Points of Interest (POIs) as well as the tourist preferences and constraints. However, in the context of next-generation touristic services, recommendation algorithms should also consider the applications (e.g., social network, mobile video streaming, mobile augmented reality) the tourist will consume in the POIs and the quality in which the MEC infrastructure will deliver such applications. In this paper, we address the joint problem of recommending personalized tour itineraries for tourists and efficiently allocating MEC resources for advanced touristic applications. We formulate an optimization problem that maximizes the itinerary of individual tourists while optimizing the resource allocation at the network edge. We then propose an exact algorithm that quickly solves the problem optimally, considering instances of realistic size. Using a real-world location-based photo-sharing database, we conduct and present an exploratory analysis to understand preferences and users' visiting patterns. Using this understanding, we propose a methodology to identify user interest in applications. Finally, we evaluate our algorithm using this dataset. Results show that our algorithm outperforms a modified version of a state-of-the-art solution for personalized tour itinerary recommendation, demonstrating gains up to 11% for resource allocation efficiency and 40% for user experience. In addition, our algorithm performs similarly to the modified state-of-the-art solution regarding traditional itinerary recommendation metrics.

Figures (16)

• Figure 1: User spots with advanced mobile networks ICT infrastructure.
• Figure 2: Tree of classification for tour-related research. Adapted from lim-tour:18. Yellow boxes indicate the topic of interest.
• Figure 3: System model.
• Figure 4: POI popularity Distribution. PDF is presented in blue, and CDF is shown in red. Values have been normalized so that the popularity of the most popular POI equals 1.
• Figure 5: POI Popularity (in blue) and number of photos taken in each POI (in red). POI popularity is given in the number of POI visits. The top 3 POIs in the number of photos are marked with stars.