15 Citations (Scopus)

Abstract

Given the users from a social network site, who have been tagged with a set of terms, how can we recommend the movies tagged with a completely different set of terms hosted by another website? Given the users from a website dedicated to Type I and Type II diabetes, how can we recommend the discussion threads from another website dedicated to gestational diabetes, where the keywords used in the two websites might be quite diverse? In other words, how can we recommend across heterogeneous domains characterized by barely overlapping feature sets? Despite the vast amount of existing work devoted to recommendation within homogeneous domains (e.g., with the same set of features), or collaborative filtering, emerging applications call for new techniques to address the problem of recommendation across heterogeneous domains, such as recommending movies hosted by one website to users from another website with barely overlapping tags. To this end, in this paper, we propose a graph-based approach for recommendation across heterogeneous domains. Specifically, for each domain, we use a bipartite graph to represent the relationships between its entities and features. Furthermore, to bridge the gap among multiple heterogeneous domains with barely overlapping sets of features, we propose to infer their semantic relatedness through concept-based interpretation distilled from online encyclopedias, e.g., Wikipedia and Baike. Finally, we propose an efficient propagation algorithm to obtain the similarity between entities from heterogeneous domains. Experimental results on both Weibo-Douban data set and Diabetes data set demonstrate the effectiveness and efficiency of our algorithm.

Original languageEnglish (US)
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
PublisherAssociation for Computing Machinery
Pages463-472
Number of pages10
Volume19-23-Oct-2015
ISBN (Print)9781450337946
DOIs
StatePublished - Oct 17 2015
Event24th ACM International Conference on Information and Knowledge Management, CIKM 2015 - Melbourne, Australia
Duration: Oct 19 2015Oct 23 2015

Other

Other24th ACM International Conference on Information and Knowledge Management, CIKM 2015
CountryAustralia
CityMelbourne
Period10/19/1510/23/15

Fingerprint

Graph
Web sites
Overlapping
Diabetes
Movies
Thread
Key words
Collaborative filtering
Tag
Propagation
Social networks
Wikipedia
Bipartite graph

Keywords

  • Cross-domain recommendation
  • Graph propagation
  • Heterogenous domains
  • Semantic matching

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Yang, D., He, J., Qin, H., Xiao, Y., & Wang, W. (2015). A graph-based recommendation across heterogeneous domains. In International Conference on Information and Knowledge Management, Proceedings (Vol. 19-23-Oct-2015, pp. 463-472). Association for Computing Machinery. https://doi.org/10.1145/2806416.2806523

A graph-based recommendation across heterogeneous domains. / Yang, Deqing; He, Jingrui; Qin, Huazheng; Xiao, Yanghua; Wang, Wei.

International Conference on Information and Knowledge Management, Proceedings. Vol. 19-23-Oct-2015 Association for Computing Machinery, 2015. p. 463-472.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yang, D, He, J, Qin, H, Xiao, Y & Wang, W 2015, A graph-based recommendation across heterogeneous domains. in International Conference on Information and Knowledge Management, Proceedings. vol. 19-23-Oct-2015, Association for Computing Machinery, pp. 463-472, 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, Australia, 10/19/15. https://doi.org/10.1145/2806416.2806523
Yang D, He J, Qin H, Xiao Y, Wang W. A graph-based recommendation across heterogeneous domains. In International Conference on Information and Knowledge Management, Proceedings. Vol. 19-23-Oct-2015. Association for Computing Machinery. 2015. p. 463-472 https://doi.org/10.1145/2806416.2806523
Yang, Deqing ; He, Jingrui ; Qin, Huazheng ; Xiao, Yanghua ; Wang, Wei. / A graph-based recommendation across heterogeneous domains. International Conference on Information and Knowledge Management, Proceedings. Vol. 19-23-Oct-2015 Association for Computing Machinery, 2015. pp. 463-472
@inproceedings{635c92fb66f84b1caca6e67bc9180c71,
title = "A graph-based recommendation across heterogeneous domains",
abstract = "Given the users from a social network site, who have been tagged with a set of terms, how can we recommend the movies tagged with a completely different set of terms hosted by another website? Given the users from a website dedicated to Type I and Type II diabetes, how can we recommend the discussion threads from another website dedicated to gestational diabetes, where the keywords used in the two websites might be quite diverse? In other words, how can we recommend across heterogeneous domains characterized by barely overlapping feature sets? Despite the vast amount of existing work devoted to recommendation within homogeneous domains (e.g., with the same set of features), or collaborative filtering, emerging applications call for new techniques to address the problem of recommendation across heterogeneous domains, such as recommending movies hosted by one website to users from another website with barely overlapping tags. To this end, in this paper, we propose a graph-based approach for recommendation across heterogeneous domains. Specifically, for each domain, we use a bipartite graph to represent the relationships between its entities and features. Furthermore, to bridge the gap among multiple heterogeneous domains with barely overlapping sets of features, we propose to infer their semantic relatedness through concept-based interpretation distilled from online encyclopedias, e.g., Wikipedia and Baike. Finally, we propose an efficient propagation algorithm to obtain the similarity between entities from heterogeneous domains. Experimental results on both Weibo-Douban data set and Diabetes data set demonstrate the effectiveness and efficiency of our algorithm.",
keywords = "Cross-domain recommendation, Graph propagation, Heterogenous domains, Semantic matching",
author = "Deqing Yang and Jingrui He and Huazheng Qin and Yanghua Xiao and Wei Wang",
year = "2015",
month = "10",
day = "17",
doi = "10.1145/2806416.2806523",
language = "English (US)",
isbn = "9781450337946",
volume = "19-23-Oct-2015",
pages = "463--472",
booktitle = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",

}

TY - GEN

T1 - A graph-based recommendation across heterogeneous domains

AU - Yang, Deqing

AU - He, Jingrui

AU - Qin, Huazheng

AU - Xiao, Yanghua

AU - Wang, Wei

PY - 2015/10/17

Y1 - 2015/10/17

N2 - Given the users from a social network site, who have been tagged with a set of terms, how can we recommend the movies tagged with a completely different set of terms hosted by another website? Given the users from a website dedicated to Type I and Type II diabetes, how can we recommend the discussion threads from another website dedicated to gestational diabetes, where the keywords used in the two websites might be quite diverse? In other words, how can we recommend across heterogeneous domains characterized by barely overlapping feature sets? Despite the vast amount of existing work devoted to recommendation within homogeneous domains (e.g., with the same set of features), or collaborative filtering, emerging applications call for new techniques to address the problem of recommendation across heterogeneous domains, such as recommending movies hosted by one website to users from another website with barely overlapping tags. To this end, in this paper, we propose a graph-based approach for recommendation across heterogeneous domains. Specifically, for each domain, we use a bipartite graph to represent the relationships between its entities and features. Furthermore, to bridge the gap among multiple heterogeneous domains with barely overlapping sets of features, we propose to infer their semantic relatedness through concept-based interpretation distilled from online encyclopedias, e.g., Wikipedia and Baike. Finally, we propose an efficient propagation algorithm to obtain the similarity between entities from heterogeneous domains. Experimental results on both Weibo-Douban data set and Diabetes data set demonstrate the effectiveness and efficiency of our algorithm.

AB - Given the users from a social network site, who have been tagged with a set of terms, how can we recommend the movies tagged with a completely different set of terms hosted by another website? Given the users from a website dedicated to Type I and Type II diabetes, how can we recommend the discussion threads from another website dedicated to gestational diabetes, where the keywords used in the two websites might be quite diverse? In other words, how can we recommend across heterogeneous domains characterized by barely overlapping feature sets? Despite the vast amount of existing work devoted to recommendation within homogeneous domains (e.g., with the same set of features), or collaborative filtering, emerging applications call for new techniques to address the problem of recommendation across heterogeneous domains, such as recommending movies hosted by one website to users from another website with barely overlapping tags. To this end, in this paper, we propose a graph-based approach for recommendation across heterogeneous domains. Specifically, for each domain, we use a bipartite graph to represent the relationships between its entities and features. Furthermore, to bridge the gap among multiple heterogeneous domains with barely overlapping sets of features, we propose to infer their semantic relatedness through concept-based interpretation distilled from online encyclopedias, e.g., Wikipedia and Baike. Finally, we propose an efficient propagation algorithm to obtain the similarity between entities from heterogeneous domains. Experimental results on both Weibo-Douban data set and Diabetes data set demonstrate the effectiveness and efficiency of our algorithm.

KW - Cross-domain recommendation

KW - Graph propagation

KW - Heterogenous domains

KW - Semantic matching

UR - http://www.scopus.com/inward/record.url?scp=84958256024&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84958256024&partnerID=8YFLogxK

U2 - 10.1145/2806416.2806523

DO - 10.1145/2806416.2806523

M3 - Conference contribution

AN - SCOPUS:84958256024

SN - 9781450337946

VL - 19-23-Oct-2015

SP - 463

EP - 472

BT - International Conference on Information and Knowledge Management, Proceedings

PB - Association for Computing Machinery

ER -