Please use this identifier to cite or link to this item: https://doi.org/10.1145/3077136.3080774
DC FieldValue
dc.titleCross-Domain Recommendation via Clustering on Multi-Layer Graphs
dc.contributor.authorAleksandr Farseev
dc.contributor.authorIvan Samborskii
dc.contributor.authorAndrey Filchenkov
dc.contributor.authorTat-Seng Chua
dc.date.accessioned2020-04-29T00:57:21Z
dc.date.available2020-04-29T00:57:21Z
dc.date.issued2017-08-07
dc.identifier.citationAleksandr Farseev, Ivan Samborskii, Andrey Filchenkov, Tat-Seng Chua (2017-08-07). Cross-Domain Recommendation via Clustering on Multi-Layer Graphs. ACM SIGIR 2017 : 195-204. ScholarBank@NUS Repository. https://doi.org/10.1145/3077136.3080774
dc.identifier.isbn9781450350228
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/167396
dc.description.abstractVenue category recommendation is an essential application for the tourism and advertisement industries, wherein it may suggest attractive localities within close proximity to users' current location. Considering that many adults use more than three social networks simultaneously, it is reasonable to leverage on this rapidly growing multi-source social media data to boost venue recommendation performance. Another approach to achieve higher recommendation results is to utilize group knowledge, which is able to diversify recommendation output. Taking into account these two aspects, we introduce a novel cross-network collaborative recommendation framework C3R, which utilizes both individual and group knowledge, while being trained on data from multiple social media sources. Group knowledge is derived based on new crosssource user community detection approach, which utilizes both inter-source relationship and the ability of sources to complement each other. To fully utilize multi-source multi-view data, we process user-generated content by employing state-of-The-Art text, image, and location processing techniques. Our experimental results demonstrate the superiority of our multi-source framework over state-of-The-Art baselines and different data source combinations. In addition, we suggest a new approach for automatic construction of inter-network relationship graph based on the data, which eliminates the necessity of having pre-defined domain knowledge. © 2017 Copyright held by the owner/author(s).
dc.publisherAssociation for Computing Machinery, Inc
dc.typeConference Paper
dc.contributor.departmentDEPARTMENT OF COMPUTER SCIENCE
dc.description.doi10.1145/3077136.3080774
dc.description.sourcetitleACM SIGIR 2017
dc.description.page195-204
dc.grant.idR-252-300-002-490
dc.grant.fundingagencyInfocomm Media Development Authority
dc.grant.fundingagencyNational Research Foundation
Appears in Collections:Staff Publications
Elements

Show simple item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Cross-Domain Recommendation via Clustering on Multi-Layer Graphs.pdf1.57 MBAdobe PDF

OPEN

NoneView/Download

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.