Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation

TL;DR

This paper introduces a Bayes-enhanced multi-view attention network to improve the robustness of POI recommendation systems by effectively handling unreliable and noisy check-in data through graph augmentation and multi-view learning.

Contribution

It proposes a novel Bayesian graph augmentation method and a multi-view attention mechanism to enhance POI recommendation robustness against data uncertainty.

Findings

Significantly outperforms state-of-the-art methods on noisy check-in data

Effective handling of incomplete and unreliable user check-ins

Improved recommendation accuracy in real-world scenarios

Abstract

POI recommendation is practically important to facilitate various Location-Based Social Network services, and has attracted rising research attention recently. Existing works generally assume the available POI check-ins reported by users are the ground-truth depiction of user behaviors. However, in real application scenarios, the check-in data can be rather unreliable due to both subjective and objective causes including positioning error and user privacy concerns, leading to significant negative impacts on the performance of the POI recommendation. To this end, we investigate a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network. Specifically, we construct personal POI transition graph, the semantic-based POI graph and distance-based POI graph to comprehensively model the dependencies among the POIs. As the personal POI transition graph is usually sparse and sensitive to noise, we design a Bayes-enhanced spatial dependency learning module for data augmentation from the local view. A Bayesian posterior guided graph augmentation approach is adopted to generate a new graph with collaborative signals to increase the data diversity. Then both the original and the augmented graphs are used for POI representation learning to counteract the data uncertainty issue. Next, the POI representations of the three view graphs are input into the proposed multi-view attention-based user preference learning module. By incorporating the semantic and distance correlations of POIs, the user preference can be effectively refined and finally robust recommendation results are achieved. The results of extensive experiments show that BayMAN significantly outperforms the state-of-the-art methods in POI recommendation when the available check-ins are incomplete and noisy.