Adversarial contrastive with leveraging negative knowledge for point of interest sequence learning.

Abstract

The core of mining point of interest (POI) data is to learn the user preference representation. However, existing POI sequence learning methods often serve downstream tasks in an end-to-end manner. It lacks the ability to support multiple downstream tasks, resulting in unsatisfactory generalization and poor performance. Besides, although POI sequence learning uses contrastive learning to learn the user preferences feature in positive and negative samples, they fail to simultaneously consider that negative samples contain useful characteristics. To improve the generalization and performance of POI sequence learning methods for various downstream tasks, we propose an Adversarial Contrastive with Leveraging Negative Knowledge model (ACLNK). First, we design an adversarial generalizing representation module for capturing the user long-term preferences to generate a generalized user historical representation incorporating user social circles. Second, to capture comprehensive short-term preferences from a limited input sequence, we design a negative sample knowledge extraction attention mechanism to absorb knowledge from negative data. Finally, the learned short- and long-term preferences as the input of the contrastive module to generate the accurate user generalization representation. We demonstrate the effectiveness and generality of ACLNK on three check-in sequence datasets for two kinds of downstream tasks. Extensive experiments demonstrate that our proposed model significantly outperforms previous state-of-the-art models. Our code is available at https://github.com/Lucas-Z9277/ACLNK_main.