Affiliation:
1. Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
Abstract
Next-point-of-interest (POI) recommendation is a crucial part of location-based social applications. Existing works have attempted to learn behavior representation through a sequence model combined with spatial-temporal-interval context. However, these approaches ignore the impact of implicit behavior patterns contained in the visit trajectory on user decision making. In this paper, we propose a novel graph self-supervised behavior pattern learning model (GSBPL) for the next-POI recommendation. GSBPL applies two graph data augmentation operations to generate augmented trajectory graphs to model implicit behavior patterns. At the same time, a graph preference representation encoder (GPRE) based on geographical and social context is proposed to learn the high-order representations of trajectory graphs, and then capture implicit behavior patterns through contrastive learning. In addition, we propose a self-attention based on multi-feature embedding to learn users’ short-term dynamic preferences, and finally combine trajectory graph representation to predict the next location. The experimental results on three real-world datasets demonstrate that GSBPL outperforms the supervised learning baseline in terms of performance under the same conditions.
Funder
Shandong Provincial Natural Science Foundation
National Natural Science Foundation of China
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
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