Context-Aware Attention-Based Data Augmentation for POI Recommendation

TL;DR

This paper introduces PA-Seq2Seq, an attention-based model that augments POI check-in data by predicting missing check-ins, improving next POI recommendation accuracy in the presence of sparse and irregular data.

Contribution

The paper proposes a novel sequence-to-sequence generative model with local attention to address data sparsity in POI recommendation tasks.

Findings

Improved recommendation accuracy on Gowalla and Brightkite datasets.

Effective handling of missing and irregular check-in data.

Enhanced model performance with time-aware attention mechanism.

Abstract

With the rapid growth of location-based social networks (LBSNs), Point-Of-Interest (POI) recommendation has been broadly studied in this decade. Recently, the next POI recommendation, a natural extension of POI recommendation, has attracted much attention. It aims at suggesting the next POI to a user in spatial and temporal context, which is a practical yet challenging task in various applications. Existing approaches mainly model the spatial and temporal information, and memorize historical patterns through user's trajectories for recommendation. However, they suffer from the negative impact of missing and irregular check-in data, which significantly influences the model performance. In this paper, we propose an attention-based sequence-to-sequence generative model, namely POI-Augmentation Seq2Seq (PA-Seq2Seq), to address the sparsity of training set by making check-in records to be evenly-spaced. Specifically, the encoder summarises each check-in sequence and the decoder predicts the possible missing check-ins based on the encoded information. In order to learn time-aware correlation among user history, we employ local attention mechanism to help the decoder focus on a specific range of context information when predicting a certain missing check-in point. Extensive experiments have been conducted on two real-world check-in datasets, Gowalla and Brightkite, for performance and effectiveness evaluation.