CAPE

Content-Aware Hierarchical Point-of-Interest Embedding Model

Recommending a point-of-interest (POI) a user will visit next based on temporal and spatial context information is an important task in mobile-based applications. Recently, several POI recommendation models based on conventional sequential-data modeling approaches have been proposed. However, such models focus on only a user's check-in sequence information and the physical distance between POIs. Furthermore, they do not utilize the characteristics of POIs or the relationships between POIs. To address this problem, we propose CAPE, the first content-aware POI embedding model which utilizes text content that provides information about the characteristics of a POI. CAPE consists of a check-in context layer and a text content layer. The check-in context layer captures the geographical influence of POIs from the check-in sequence of a user, while the text content layer captures the characteristics of POIs from the text content. To validate the efficacy of CAPE, we constructed a large-scale POI dataset. In the experimental evaluation, we show that the performance of the existing POI recommendation models can be significantly improved by simply applying CAPE to the models.

Dataset

Instagram Check-in Dataset

Since most of the existing POI recommendation studies do not use text content, there is no suitable dataset that contains text content. Although Yang et al. [Yang et al., 2013] use text content to understand user sentiment and improve the POI recommendation performance, their dataset is unsuitable for learning POI representations. First, many POIs in their dataset do not have text contents written by users. Second, the dataset is not large enough for training POI representations. Therefore, we constructed a new dataset that contains text contents which refer to POIs. We collected data from Instagram, which is one of the most popular mobile-based social networks. Instagram data includes not only user POI check-in information, but also text content written by users. We collected Instagram data created in New York City and preprocessed the collected data utilizing the same method of Zhao et al. [Zhao et al., 2017]. We removed the POIs with less than five checked-ins and the users who had less than ten posts. After preprocessing, our new dataset includes 2,216,631 check-ins at 13,187 POIs of 78,233 users. Please download the Dataset here.

Please cite our paper if you publish material based on this dataset.

[Chang et al., 2018] Buru Chang, Yonggyu Park, Donghyeon Park, Seongsoon Kim and Jaewoo Kang. Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation. In the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018).

Source Code

PyTorch Implementation

Source code is available here.

Reference

  • [Yang et al., 2013] Dingqi Yang, Daqing Zhang, Zhiyong Yu, and Zhu Wang. A sentiment-enhanced personalized location recommendation system. In Proceedings of the 24th ACM Conference on Hypertext and Social Media, pages 119–128. ACM, 2013.

  • [Zhao et al., 2017] Shenglin Zhao, Tong Zhao, Irwin King, and Michael R Lyu. Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation. In Proceedings of the 26th International Conference on World Wide Web Companion, pages 153–162. International World Wide Web Conferences Steering Committee, 2017.