A Category-Aware Deep Model for Successive POI Recommendation on Sparse Check-in Data

Fuqiang Yu, Lizhen Cui, Wei Guo, Xudong Lu, Qingzhong Li, Hua Lu

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

As considerable amounts of POI check-in data have been accumulated, successive point-of-interest (POI) recommendation is increasingly popular. Existing successive POI recommendation methods only predict where user will go next, ignoring when this behavior will occur. In this work, we focus on predicting POIs that will be visited by users in the next 24 hours. As check-in data is very sparse, it is challenging to accurately capture user preferences in temporal patterns. To this end, we propose a category-aware deep model CatDM that incorporates POI category and geographical influence to reduce search space to overcome data sparsity. We design two deep encoders based on LSTM to model the time series data. The first encoder captures user preferences in POI categories, whereas the second exploits user preferences in POIs. Considering clock influence in the second encoder, we divide each user’s check-in history into several different time windows and develop a personalized attention mechanism for each window to facilitate CatDM to exploit temporal patterns. Moreover, to sort the candidate set, we consider four specific dependencies: user-POI, user-category, POI-time and POI-user current preferences. Extensive experiments are conducted on two large real datasets. The experimental results demonstrate that our CatDM outperforms the state-of-the-art models for successive POI recommendation on sparse check-in data.
Original languageEnglish
Title of host publicationWWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020
EditorsYennun Huang, Irwin King, Tie-Yan Liu, Maarten van Steen
Number of pages11
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Publication date2020
Pages1264-1274
ISBN (Electronic)978-1-4503-7023-3
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventThe World Wde Web Conference 2020 (online) - Online, Taipei, Taiwan, Province of China
Duration: 20 Apr 202024 Apr 2020

Conference

ConferenceThe World Wde Web Conference 2020 (online)
LocationOnline
Country/TerritoryTaiwan, Province of China
CityTaipei
Period20/04/202024/04/2020

Cite this