Show simple item record

dc.contributor.authorHan, Peng
dc.contributor.authorLi, Zhongxiao
dc.contributor.authorLiu, Yong
dc.contributor.authorZhao, Peilin
dc.contributor.authorLi, Jing
dc.contributor.authorWang, Hao
dc.contributor.authorShang, Shuo
dc.date.accessioned2021-02-22T08:18:42Z
dc.date.available2021-02-22T08:18:42Z
dc.date.issued2020-07
dc.identifier.citationHan, P., Li, Z., Liu, Y., Zhao, P., Li, J., Wang, H., & Shang, S. (2020). Contextualized Point-of-Interest Recommendation. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. doi:10.24963/ijcai.2020/344
dc.identifier.isbn9780999241165
dc.identifier.doi10.24963/ijcai.2020/344
dc.identifier.urihttp://hdl.handle.net/10754/667564
dc.description.abstractPoint-of-interest (POI) recommendation has become an increasingly important sub-field of recommendation system research. Previous methods employ various assumptions to exploit the contextual information for improving the recommendation accuracy. The common property among them is that similar users are more likely to visit similar POIs and similar POIs would like to be visited by the same user. However, none of existing methods utilize similarity explicitly to make recommendations. In this paper, we propose a new framework for POI recommendation, which explicitly utilizes similarity with contextual information. Specifically, we categorize the context information into two groups, i.e., global and local context, and develop different regularization terms to incorporate them for recommendation. A graph Laplacian regularization term is utilized to exploit the global context information. Moreover, we cluster users into different groups, and let the objective function constrain the users in the same group to have similar predicted POI ratings. An alternating optimization method is developed to optimize our model and get the final rating matrix. The results in our experiments show that our algorithm outperforms all the state-of-the-art methods.
dc.description.sponsorshipThis work is supported by the National Natural Science Foundation of China (No. 61932004).
dc.publisherInternational Joint Conferences on Artificial Intelligence
dc.relation.urlhttps://www.ijcai.org/proceedings/2020/344
dc.relation.urlhttps://www.ijcai.org/proceedings/2020/0344.pdf
dc.rightsArchived with thanks to International Joint Conferences on Artificial Intelligence Organization
dc.titleContextualized Point-of-Interest Recommendation
dc.typeConference Paper
dc.contributor.departmentKing Abdullah University of Science and Technology
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.nameTwenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20)
dc.eprint.versionPre-print
dc.contributor.institutionNanyang Technological University
dc.contributor.institutionTencent AI Lab
dc.contributor.institutionInception Institute of Artificial Intelligence
dc.contributor.institutionUniversity of Electronic Science and Technology of China
kaust.personHan, Peng
kaust.personLi, Zhongxiao
refterms.dateFOA2021-02-22T08:19:31Z


Files in this item

Thumbnail
Name:
Conference Paperfile1.pdf
Size:
915.5Kb
Format:
PDF
Description:
Pre-print

This item appears in the following Collection(s)

Show simple item record