Merge pull request #30 from WING-NUS/hugoblox-import-publications

Hugo Blox Builder - Import latest publications

content/publication/10-1002-asi-23834/cite.bib

@@ -0,0 +1,18 @@
+@article{10.1002/asi.23834,
+ abstract = {Big Science and cross-disciplinary collaborations have reshaped the intellectual structure of research areas. A number of works have tried to uncover this hidden intellectual structure by analyzing citation contexts. However, none of them analyzed by document logical structures such as sections. The two major goals of this study are to find characteristics of authors who are highly cited section-wise and to identify the differences in section-wise author networks. This study uses 29,158 of research articles culled from the ACL Anthology, which hosts articles on computational linguistics and natural language processing. We find that the distribution of citations across sections is skewed and that a different set of highly cited authors share distinct academic characteristics, according to their citation locations. Furthermore, the author networks based on citation context similarity reveal that the intellectual structure of a domain differs across different sections.},
+ address = {USA},
+ author = {An, Juyoung and Kim, Namhee and Kan, Min-Yen and Chandrasekaran, Muthu Kumar and Song, Min},
+ doi = {10.1002/asi.23834},
+ issn = {2330-1635},
+ issue_date = {August 2017},
+ journal = {J. Assoc. Inf. Sci. Technol.},
+ month = {August},
+ number = {8},
+ numpages = {14},
+ pages = {1975–1988},
+ publisher = {John Wiley & Sons, Inc.},
+ title = {Exploring characteristics of highly cited authors according to citation location and content},
+ url = {https://doi.org/10.1002/asi.23834},
+ volume = {68},
+ year = {2017}
+}

content/publication/10-1002-asi-23834/index.md

@@ -0,0 +1,31 @@
+---
+title: Exploring characteristics of highly cited authors according to citation location
+  and content
+authors:
+- Juyoung An
+- Namhee Kim
+- Min-Yen Kan
+- Muthu Kumar Chandrasekaran
+- Min Song
+date: '2017-08-01'
+publishDate: '2024-10-19T14:08:42.168581Z'
+publication_types:
+- article-journal
+publication: '*J. Assoc. Inf. Sci. Technol.*'
+doi: 10.1002/asi.23834
+abstract: Big Science and cross-disciplinary collaborations have reshaped the intellectual
+  structure of research areas. A number of works have tried to uncover this hidden
+  intellectual structure by analyzing citation contexts. However, none of them analyzed
+  by document logical structures such as sections. The two major goals of this study
+  are to find characteristics of authors who are highly cited section-wise and to
+  identify the differences in section-wise author networks. This study uses 29,158
+  of research articles culled from the ACL Anthology, which hosts articles on computational
+  linguistics and natural language processing. We find that the distribution of citations
+  across sections is skewed and that a different set of highly cited authors share
+  distinct academic characteristics, according to their citation locations. Furthermore,
+  the author networks based on citation context similarity reveal that the intellectual
+  structure of a domain differs across different sections.
+links:
+- name: URL
+  url: https://doi.org/10.1002/asi.23834
+---

content/publication/10-1007-978-3-319-48051-0-15/cite.bib

@@ -0,0 +1,16 @@
+@inproceedings{10.1007/978-3-319-48051-0_15,
+ abstract = {Among several traditional and novel mobile app recommender techniques that utilize a diverse set of app-related features (such as an app’s Twitter followers, various version instances, etc.), which app-related features are the most important indicators for app recommendation? In this paper, we develop a hybrid app recommender framework that integrates a variety of app-related features and recommendation techniques, and then identify the most important indicators for the app recommendation task. Our results reveal an interesting correlation with data from third-party app analytics companies; and suggest that, in the context of mobile app recommendation, more focus could be placed in user and trend analysis via social networks.},
+ address = {Berlin, Heidelberg},
+ author = {Lin, Jovian and Sugiyama, Kazunari and Kan, Min-Yen and Chua, Tat-Seng},
+ booktitle = {Information Retrieval Technology: 12th Asia Information Retrieval Societies Conference, AIRS 2016, Beijing, China, November 30 – December 2, 2016, Proceedings},
+ doi = {10.1007/978-3-319-48051-0_15},
+ isbn = {978-3-319-48050-3},
+ keywords = {Recommender systems, Mobile apps, Gradient tree boosting},
+ location = {Beijing, China},
+ numpages = {15},
+ pages = {197–211},
+ publisher = {Springer-Verlag},
+ title = {Scrutinizing Mobile App Recommendation: Identifying Important App-Related Indicators},
+ url = {https://doi.org/10.1007/978-3-319-48051-0_15},
+ year = {2016}
+}

content/publication/10-1007-978-3-319-48051-0-15/index.md

@@ -0,0 +1,32 @@
+---
+title: 'Scrutinizing Mobile App Recommendation: Identifying Important App-Related
+  Indicators'
+authors:
+- Jovian Lin
+- Kazunari Sugiyama
+- Min-Yen Kan
+- Tat-Seng Chua
+date: '2016-01-01'
+publishDate: '2024-10-19T14:08:42.190112Z'
+publication_types:
+- paper-conference
+publication: '*Information Retrieval Technology: 12th Asia Information Retrieval Societies
+  Conference, AIRS 2016, Beijing, China, November 30 – December 2, 2016, Proceedings*'
+doi: 10.1007/978-3-319-48051-0_15
+abstract: Among several traditional and novel mobile app recommender techniques that
+  utilize a diverse set of app-related features (such as an app’s Twitter followers,
+  various version instances, etc.), which app-related features are the most important
+  indicators for app recommendation? In this paper, we develop a hybrid app recommender
+  framework that integrates a variety of app-related features and recommendation techniques,
+  and then identify the most important indicators for the app recommendation task.
+  Our results reveal an interesting correlation with data from third-party app analytics
+  companies; and suggest that, in the context of mobile app recommendation, more focus
+  could be placed in user and trend analysis via social networks.
+tags:
+- Recommender systems
+- Mobile apps
+- Gradient tree boosting
+links:
+- name: URL
+  url: https://doi.org/10.1007/978-3-319-48051-0_15
+---

content/publication/10-1007-s-00799-014-0122-2/cite.bib

@@ -0,0 +1,19 @@
+@article{10.1007/s00799-014-0122-2,
+ abstract = {To help generate relevant suggestions for researchers, recommendation systems have started to leverage the latent interests in the publication profiles of the researchers themselves. While using such a publication citation network has been shown to enhance performance, the network is often sparse, making recommendation difficult. To alleviate this sparsity, in our former work, we identified "potential citation papers" through the use of collaborative filtering. Also, as different logical sections of a paper have different significance, as a secondary contribution, we investigated which sections of papers can be leveraged to represent papers effectively. While this initial approach works well for researchers vested in a single discipline, it generates poor predictions for scientists who work on several different topics in the discipline (hereafter, "intra-disciplinary"). We thus extend our previous work in this paper by proposing an adaptive neighbor selection method to overcome this problem in our imputation-based collaborative filtering framework. On a publicly-available scholarly paper recommendation dataset, we show that recommendation accuracy significantly outperforms state-of-the-art recommendation baselines as measured by nDCG and MRR, when using our adaptive neighbor selection method. While recommendation performance is enhanced for all researchers, improvements are more marked for intra-disciplinary researchers, showing that our method does address the targeted audience.},
+ address = {Berlin, Heidelberg},
+ author = {Sugiyama, Kazunari and Kan, Min-Yen},
+ doi = {10.1007/s00799-014-0122-2},
+ issn = {1432-5012},
+ issue_date = {June      2015},
+ journal = {Int. J. Digit. Libr.},
+ keywords = {Recommendation, Information retrieval, Digital library, Collaborative filtering, Citation analysis},
+ month = {June},
+ number = {2},
+ numpages = {19},
+ pages = {91–109},
+ publisher = {Springer-Verlag},
+ title = {A comprehensive evaluation of scholarly paper recommendation using potential citation papers},
+ url = {https://doi.org/10.1007/s00799-014-0122-2},
+ volume = {16},
+ year = {2015}
+}

content/publication/10-1007-s-00799-014-0122-2/index.md

@@ -0,0 +1,40 @@
+---
+title: A comprehensive evaluation of scholarly paper recommendation using potential
+  citation papers
+authors:
+- Kazunari Sugiyama
+- Min-Yen Kan
+date: '2015-06-01'
+publishDate: '2024-10-19T14:08:42.212532Z'
+publication_types:
+- article-journal
+publication: '*Int. J. Digit. Libr.*'
+doi: 10.1007/s00799-014-0122-2
+abstract: To help generate relevant suggestions for researchers, recommendation systems
+  have started to leverage the latent interests in the publication profiles of the
+  researchers themselves. While using such a publication citation network has been
+  shown to enhance performance, the network is often sparse, making recommendation
+  difficult. To alleviate this sparsity, in our former work, we identified \"potential
+  citation papers\" through the use of collaborative filtering. Also, as different
+  logical sections of a paper have different significance, as a secondary contribution,
+  we investigated which sections of papers can be leveraged to represent papers effectively.
+  While this initial approach works well for researchers vested in a single discipline,
+  it generates poor predictions for scientists who work on several different topics
+  in the discipline (hereafter, \"intra-disciplinary\"). We thus extend our previous
+  work in this paper by proposing an adaptive neighbor selection method to overcome
+  this problem in our imputation-based collaborative filtering framework. On a publicly-available
+  scholarly paper recommendation dataset, we show that recommendation accuracy significantly
+  outperforms state-of-the-art recommendation baselines as measured by nDCG and MRR,
+  when using our adaptive neighbor selection method. While recommendation performance
+  is enhanced for all researchers, improvements are more marked for intra-disciplinary
+  researchers, showing that our method does address the targeted audience.
+tags:
+- Recommendation
+- Information retrieval
+- Digital library
+- Collaborative filtering
+- Citation analysis
+links:
+- name: URL
+  url: https://doi.org/10.1007/s00799-014-0122-2
+---

content/publication/10-1145-2756406-2756946/cite.bib

@@ -0,0 +1,17 @@
+@inproceedings{10.1145/2756406.2756946,
+ abstract = {We address the tasks of recovering bibliographic and document structure metadata from scholarly documents. We leverage higher order semi-Markov conditional random fields to model long-distance label sequences, improving upon the performance of the linear-chain conditional random field model. We introduce the notion of extensible features, which allows the expensive inference process to be simplified through memoization, resulting in lower computational complexity. Our method significantly betters the state-of-the-art on three related scholarly document extraction tasks.},
+ address = {New York, NY, USA},
+ author = {Cuong, Nguyen Viet and Chandrasekaran, Muthu Kumar and Kan, Min-Yen and Lee, Wee Sun},
+ booktitle = {Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries},
+ doi = {10.1145/2756406.2756946},
+ isbn = {9781450335942},
+ keywords = {metadata extraction, logical structure discovery, conditional random fields},
+ location = {Knoxville, Tennessee, USA},
+ numpages = {4},
+ pages = {61–64},
+ publisher = {Association for Computing Machinery},
+ series = {JCDL '15},
+ title = {Scholarly Document Information Extraction using Extensible Features for Efficient Higher Order Semi-CRFs},
+ url = {https://doi.org/10.1145/2756406.2756946},
+ year = {2015}
+}

content/publication/10-1145-2756406-2756946/index.md

@@ -0,0 +1,29 @@
+---
+title: Scholarly Document Information Extraction using Extensible Features for Efficient
+  Higher Order Semi-CRFs
+authors:
+- Nguyen Viet Cuong
+- Muthu Kumar Chandrasekaran
+- Min-Yen Kan
+- Wee Sun Lee
+date: '2015-01-01'
+publishDate: '2024-10-19T14:08:42.182824Z'
+publication_types:
+- paper-conference
+publication: '*Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries*'
+doi: 10.1145/2756406.2756946
+abstract: We address the tasks of recovering bibliographic and document structure
+  metadata from scholarly documents. We leverage higher order semi-Markov conditional
+  random fields to model long-distance label sequences, improving upon the performance
+  of the linear-chain conditional random field model. We introduce the notion of extensible
+  features, which allows the expensive inference process to be simplified through
+  memoization, resulting in lower computational complexity. Our method significantly
+  betters the state-of-the-art on three related scholarly document extraction tasks.
+tags:
+- metadata extraction
+- logical structure discovery
+- conditional random fields
+links:
+- name: URL
+  url: https://doi.org/10.1145/2756406.2756946
+---

content/publication/10-1145-2808797-2808820/cite.bib

@@ -0,0 +1,17 @@
+@inproceedings{10.1145/2808797.2808820,
+ abstract = {We study how users of multiple online social networks (OSNs) employ and share information by studying a common user pool that use six OSNs -- Flickr, Google+, Instagram, Tumblr, Twitter, and YouTube. We analyze the temporal and topical signature of users' sharing behaviour, showing how they exhibit distinct behaviorial patterns on different networks. We also examine cross-sharing (i.e., the act of user broadcasting their activity to multiple OSNs near-simultaneously), a previously-unstudied behaviour and demonstrate how certain OSNs play the roles of originating source and destination sinks.},
+ address = {New York, NY, USA},
+ author = {Lim, Bang Hui and Lu, Dongyuan and Chen, Tao and Kan, Min-Yen},
+ booktitle = {Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015},
+ doi = {10.1145/2808797.2808820},
+ isbn = {9781450338547},
+ keywords = {Online Social Networks, cross-sharing, user behaviour},
+ location = {Paris, France},
+ numpages = {8},
+ pages = {113–120},
+ publisher = {Association for Computing Machinery},
+ series = {ASONAM '15},
+ title = {#mytweet via Instagram: Exploring User Behaviour across Multiple Social Networks},
+ url = {https://doi.org/10.1145/2808797.2808820},
+ year = {2015}
+}

content/publication/10-1145-2808797-2808820/index.md

@@ -0,0 +1,30 @@
+---
+title: '#mytweet via Instagram: Exploring User Behaviour across Multiple Social Networks'
+authors:
+- Bang Hui Lim
+- Dongyuan Lu
+- Tao Chen
+- Min-Yen Kan
+date: '2015-01-01'
+publishDate: '2024-10-19T14:08:42.205353Z'
+publication_types:
+- paper-conference
+publication: '*Proceedings of the 2015 IEEE/ACM International Conference on Advances
+  in Social Networks Analysis and Mining 2015*'
+doi: 10.1145/2808797.2808820
+abstract: We study how users of multiple online social networks (OSNs) employ and
+  share information by studying a common user pool that use six OSNs -- Flickr, Google+,
+  Instagram, Tumblr, Twitter, and YouTube. We analyze the temporal and topical signature
+  of users' sharing behaviour, showing how they exhibit distinct behaviorial patterns
+  on different networks. We also examine cross-sharing (i.e., the act of user broadcasting
+  their activity to multiple OSNs near-simultaneously), a previously-unstudied behaviour
+  and demonstrate how certain OSNs play the roles of originating source and destination
+  sinks.
+tags:
+- Online Social Networks
+- cross-sharing
+- user behaviour
+links:
+- name: URL
+  url: https://doi.org/10.1145/2808797.2808820
+---

content/publication/10-1145-2911451-2914698/cite.bib

@@ -0,0 +1,17 @@
+@inproceedings{10.1145/2911451.2914698,
+ abstract = {Many organizations possess social media accounts on different social networks, but these profiles are not always linked. End applications, users, as well as the organization themselves, can benefit when the profiles are appropriately identified and linked. Most existing works on social network entity linking focus on linking individuals, and do not model features specific for organizational linking. We address this gap not only to link official social media accounts but also to discover and solve the identification and linking of associated affiliate accounts -- such as geographical divisions and brands -- which are important to distinguish.We instantiate our method for classifying profiles on social network services for Twitter and Facebook, which major organizations use. We classify profiles as to whether they belong to an organization or its affiliates. Our best classifier achieves an accuracy of 0.976 on average in both datasets, significantly improving baselines that exploit the features used in state-of-the-art comparable user linkage strategies.},
+ address = {New York, NY, USA},
+ author = {Cheng, Jerome and Sugiyama, Kazunari and Kan, Min-Yen},
+ booktitle = {Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval},
+ doi = {10.1145/2911451.2914698},
+ isbn = {9781450340694},
+ keywords = {organization entity profiling, organizational social profiles, record linkage, social networks},
+ location = {Pisa, Italy},
+ numpages = {4},
+ pages = {901–904},
+ publisher = {Association for Computing Machinery},
+ series = {SIGIR '16},
+ title = {Linking Organizational Social Network Profiles},
+ url = {https://doi.org/10.1145/2911451.2914698},
+ year = {2016}
+}

content/publication/10-1145-2911451-2914698/index.md

@@ -0,0 +1,35 @@
+---
+title: Linking Organizational Social Network Profiles
+authors:
+- Jerome Cheng
+- Kazunari Sugiyama
+- Min-Yen Kan
+date: '2016-01-01'
+publishDate: '2024-10-19T14:08:42.197769Z'
+publication_types:
+- paper-conference
+publication: '*Proceedings of the 39th International ACM SIGIR Conference on Research
+  and Development in Information Retrieval*'
+doi: 10.1145/2911451.2914698
+abstract: Many organizations possess social media accounts on different social networks,
+  but these profiles are not always linked. End applications, users, as well as the
+  organization themselves, can benefit when the profiles are appropriately identified
+  and linked. Most existing works on social network entity linking focus on linking
+  individuals, and do not model features specific for organizational linking. We address
+  this gap not only to link official social media accounts but also to discover and
+  solve the identification and linking of associated affiliate accounts -- such as
+  geographical divisions and brands -- which are important to distinguish.We instantiate
+  our method for classifying profiles on social network services for Twitter and Facebook,
+  which major organizations use. We classify profiles as to whether they belong to
+  an organization or its affiliates. Our best classifier achieves an accuracy of 0.976
+  on average in both datasets, significantly improving baselines that exploit the
+  features used in state-of-the-art comparable user linkage strategies.
+tags:
+- organization entity profiling
+- organizational social profiles
+- record linkage
+- social networks
+links:
+- name: URL
+  url: https://doi.org/10.1145/2911451.2914698
+---