Paper Info
Title
A Spatio-temporal Recommender System for On-demand Cinemas
Abstract
On-demand cinemas are a new type of offline entertainment venues which have shown the rapid expansion in the recent years. Recommending movies of interest to the potential audiences in on-demand cinemas is keen but challenging because the recommendation scenario is totally different from all the existing recommendation applications including online video recommendation, offline item recommendation and group recommendation. In this paper, we propose a novel spatio-temporal approach called Pegasus. Because of the specific characteristics of on-demand cinema recommendation, Pegasus exploits the POI (Point of Interest) information around cinemas and the content descriptions of movies, apart from the historical movie consumption records of cinemas. Pegasus explores the temporal dynamics and spatial influences rooted in audience behaviors, and captures the similarities between cinemas, the changes of audience crowds, time-varying features and regional disparities of movie popularity. It offers an effective and explainable way to recommend movies to on-demand cinemas. The corresponding Pegasus system has been deployed in some pilot on-demand cinemas. Based on the real-world data from on-demand cinemas, extensive experiments as well as pilot tests are conducted. Both experimental results and post-deployment feedback show that Pegasus is effective.
Year
DOI
Venue
2019
10.1145/3357384.3357888
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
Keywords
Field
DocType
on-demand cinema, recommender system, spatio-temporal effect
Recommender system,Movie theater,On demand,Information retrieval,Computer science
Conference
ISBN
Citations 
PageRank 
978-1-4503-6976-3
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Taofeng Xue101.01
Beihong Jin240549.23
Beibei Li301.69
Weiqing Wang429815.69
Qi Zhang500.34
Sihua Tian600.34