NLPIR SEMINAR Y2019#25 – NLPIR自然语言处理与信息检索共享平台

自然语言处理与信息检索共享平台 自然语言处理与信息检索共享平台

NLPIR SEMINAR Y2019#25

NLPIR SEMINAR Y2019#25

INTRO

In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his/her related research with you.

Arrangement

Next week’s seminar is organized as follows:

  1. The seminar time is 1.pm, Mon (August 12, 2019), at Zhongguancun Technology Park ,Building 5, 1306.
  2. Zhaoyou Liu will give a lecture about his research, the paper’s title is – Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation .
  3. The seminar will be hosted by Baohua Zhang.

Everyone interested in this topic is welcomed to join us.

Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation

Pengpeng Zhao, Haifeng Zhu, Yanchi Liu, Zhixu Li, Jiajie Xu, Victor S. Sheng

Abstract

Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. Recently Recurrent Neural Networks (RNNs) have been proved to be effective on sequential recommendation tasks. However, existing RNN solutions rarely consider the spatio-temporal intervals between neighbor checkins, which are essential for modeling user check-in behaviors in next POI recommendation. In this paper, we propose a new variant of LSTM, named STLSTM, which implements time gates and distance gates into LSTM to capture the spatio-temporal relation between successive check-ins. Specifically, one time gate and one distance gate are designed to control short-term interest update, and another time gate and distance gate are designed to control long-term interest update. Furthermore, to reduce the number of parameters and improve efficiency, we further integrate coupled input and forget gates with our proposed model. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. Our experimental results show that our model significantly outperforms the state-of-the-art approaches for next POI recommendation.

You May Also Like

About the Author: nlpvv

发表评论