Personalized Next Point-of-Interest Recommendation via Latent Behavior Patterns Inference

TL;DR

This paper introduces a novel tensor-based model for personalized next POI recommendation that captures latent behavior patterns and contextual influences, significantly improving accuracy over existing methods.

Contribution

It proposes a third-rank tensor model integrating categorical influence and personalized mobility patterns, optimized with BPR and EM, for enhanced POI recommendation.

Findings

Model outperforms state-of-the-art methods on large-scale datasets

Incorporating latent behavior patterns improves recommendation accuracy

Personalized contextual modeling enhances user-specific predictions

Abstract

In this paper, we address the problem of personalized next Point-of-interest (POI) recommendation which has become an important and very challenging task for location-based social networks (LBSNs), but not well studied yet. With the conjecture that, under different contextual scenarios, human exhibits distinct mobility pattern, we attempt here to jointly model the next POI recommendation under the influence of user's latent behavior pattern. We propose to adopt a third-rank tensor to model the successive check-in behaviors. By integrating categorical influence into mobility patterns and aggregating user's spatial preference on a POI, the proposed model deal with the next new POI recommendation problem by nature. By incorporating softmax function to fuse the personalized Markov chain with latent pattern, we furnish a Bayesian Personalized Ranking (BPR) approach and derive the optimization criterion accordingly. Expectation Maximization (EM) is then used to estimate the model parameters. We further develop a personalized model by taking into account personalized mobility patterns under the contextual scenario to improve the recommendation performance. Extensive experiments on two large-scale LBSNs datasets demonstrate the significant improvements of our model over several state-of-the-art methods.