Earticle

초록

영어

With the rapid development of social media, users' next location prediction has become an important research direction, which can provide personalized travel suggestions for users. However, existing methods ignore the semantic relationship between users' historical and current trajectories. This paper proposes a new method for predicting the user's next location to solve this problem. We first process the user POI data as trajectory data, use the attention mechanism to extract similar features of the user's historical trajectories, and then combine them with the current trajectory features to obtain the user's next location recommendation. The experimental results show that our proposed model performs satisfactorily on a real dataset.

목차

Abstract
I. INTRODUCTION
II. PROBLEM FORMULATION
A. Definition
B. Problem
III. THE PROPOSED MODEL
A. Embedding
B. Trajectory Feature Extraction Module
C. Trajectory Similarity Weighting Module
D. Prediction module
IV. EXPERIMENTAL RESULT
V. CONCLUSION
REFERENCES

키워드

저자

• Yixuan Ge [ Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing, China ] Corresponding Author
• Pengcheng Li [ Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004