The tourist trip design problem with POI categories via an Expectation-Maximization based method

Introduction

Figure 1: The schema of the proposed system architecture.

We propose awe propose an efficient deterministic method based on Expectation-Maximization (EM) to solve the challenging problem of the tourist trip design or Personalized Itinerary Recommendation (PIR) with POI categories. PIR aims to recommend a personalized tour that consists of a sequence of Points of Interest (POIs), which maximizes user satisfaction and adheres to user time budget constraints (see Figs. 1,2).

Methodology

  • This framework mainly focuses on the POIs sequence selection problem exploiting the personalized POI recommendations provided by a recommender system.

  • The proposed method sequentially solves the PIR problem by providing in each step the POI that is expected to maximize a suitable objective function, taking into account user satisfaction, user time budget, POIs opening hours, POIs category constraints and spatial constraints (e.g. start and end point, POIs locations, etc).

Fig. 2. A map of 16 POIs, where each POI is drawn by a circle. The size and the color of a POI correspond to the duration of the visit and the gained user satisfaction, respectively. The category of each POI is shown in parenthesis. The itinerary is indicated with a red line. The timetable of the personalized itinerary (bottom right).

Experiments - Downloads

    • You can download the matlab code of the PIREM, M-PIREM methods proposed in [1].

    • You can download the datasets of the method proposed in [1] from (LINK).

    • The code and extra material presented in [2] will be available after the publication of [2].

Related Publications

[1] C. Panagiotakis, E. Daskalaki H. Papadakis, and P. Fragopoulou, The tourist trip design problem with POI categories via an Expectation-Maximization based method, RecSys Workshop on Recommenders in Tourism, 2022.

[2] C. Panagiotakis, E. Daskalaki H. Papadakis, and P. Fragopoulou, An Expectation-Maximization framework for Personalized Itinerary Recommendation with POI Categories, Algorithms, 2022 (under review).