Personalised Travel Recommendation based on Location Co-occurrence

TL;DR

This paper introduces a personalized travel recommendation system that uses location co-occurrence data and Gaussian density estimation to suggest related landmarks, outperforming popularity-based methods especially with user-specific filters.

Contribution

It presents a novel location similarity model based on user visit co-occurrence and Gaussian clustering, enabling personalized and serendipitous travel recommendations.

Findings

The model outperforms popularity-based ranking in city and country scales.

Filtering by visit duration improves personalization accuracy.

RankDiff and cosine similarity yield more serendipitous recommendations.

Abstract

We propose a new task of recommending touristic locations based on a user's visiting history in a geographically remote region. This can be used to plan a touristic visit to a new city or country, or by travel agencies to provide personalised travel deals. A set of geotags is used to compute a location similarity model between two different regions. The similarity between two landmarks is derived from the number of users that have visited both places, using a Gaussian density estimation of the co-occurrence space of location visits to cluster related geotags. The standard deviation of the kernel can be used as a scale parameter that determines the size of the recommended landmarks. A personalised recommendation based on the location similarity model is evaluated on city and country scale and is able to outperform a location ranking based on popularity. Especially when a tourist filter based on visit duration is enforced, the prediction can be accurately adapted to the preference of the user. An extensive evaluation based on manual annotations shows that more strict ranking methods like cosine similarity and a proposed RankDiff algorithm provide more serendipitous recommendations and are able to link similar locations on opposite sides of the world.