Learning Points and Routes to Recommend Trajectories

TL;DR

This paper presents a machine learning approach that combines POI ranking and transition patterns to recommend personalized travel trajectories, improving accuracy over recent methods.

Contribution

It introduces a unified framework integrating POI features and transition data, along with a probabilistic model and a new F1 score for better route recommendation.

Findings

Improved trajectory recommendation accuracy over recent methods.

Effective integration of POI features and transition data.

Demonstrated benefits of combining points and routes in recommendations.

Abstract

The problem of recommending tours to travellers is an important and broadly studied area. Suggested solutions include various approaches of points-of-interest (POI) recommendation and route planning. We consider the task of recommending a sequence of POIs, that simultaneously uses information about POIs and routes. Our approach unifies the treatment of various sources of information by representing them as features in machine learning algorithms, enabling us to learn from past behaviour. Information about POIs are used to learn a POI ranking model that accounts for the start and end points of tours. Data about previous trajectories are used for learning transition patterns between POIs that enable us to recommend probable routes. In addition, a probabilistic model is proposed to combine the results of POI ranking and the POI to POI transitions. We propose a new F$_1$ score on pairs of POIs that capture the order of visits. Empirical results show that our approach improves on recent methods, and demonstrate that combining points and routes enables better trajectory recommendations.