Personalized Context-Aware Point of Interest Recommendation

TL;DR

This paper introduces a probabilistic model and a new dataset to improve personalized POI recommendations by capturing location relevance and user preferences, effectively addressing data sparsity and outperforming existing methods.

Contribution

It proposes a novel probabilistic mapping between user tags and location keywords, along with a dataset for contextual relevance, enhancing POI recommendation accuracy.

Findings

The approach outperforms state-of-the-art methods on TREC datasets.

Incorporating new contextual information improves recommendation relevance.

The models effectively address data sparsity in POI recommendation.

Abstract

Personalized recommendation of Points of Interest (POIs) plays a key role in satisfying users on Location-Based Social Networks (LBSNs). In this paper, we propose a probabilistic model to find the mapping between user-annotated tags and locations' taste keywords. Furthermore, we introduce a dataset on locations' contextual appropriateness and demonstrate its usefulness in predicting the contextual relevance of locations. We investigate four approaches to use our proposed mapping for addressing the data sparsity problem: one model to reduce the dimensionality of location taste keywords and three models to predict user tags for a new location. Moreover, we present different scores calculated from multiple LBSNs and show how we incorporate new information from the mapping into a POI recommendation approach. Then, the computed scores are integrated using learning to rank techniques. The experiments on two TREC datasets show the effectiveness of our approach, beating state-of-the-art methods.