{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90825"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90825","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Publication venue recommendation in heterogeneous information networks","abstract":"When a new paper is completed, choosing a good conference or journal in which to publish this new paper is of critical importance to all researchers. Authors often make their decision based on the topics suitability between the paper content and target venues, the likelihood of getting accepted into the venues, the publication history of the authors and other reasonable considerations. A good number of works do content-based analysis to match the topics of the paper and target venues. Such approaches often use full texts, abstracts along with other meta data. The main challenge, for this line of works, is to resolve topic ambiguity because many venues share similar topics and topics evolve over time. Another line of works are network-based approaches, which make recommendations using co-author networks and author-venue links in the bibliographic information networks. However, we have not yet seen a general framework that incorporates a broad range of both content-based features and network-based features, which are potentially capable of delivering more information to help solve the problem. In this thesis, we propose a general framework to automatically find appropriate venues for a new paper using a heterogeneous information network approach. First, meta path-based topological features are systematically extracted from the underlying bibliographic network. Then, a supervised model is used to learn the weights associated with different topological features in deciding the most suitable venues. Experiments on Microsoft Academic Graph(MAG) datasets show that our new approach consistently outperforms existing works by venue prediction accuracy. Results also show that not only topics information but authors' networks and publication history are important factors in the the problem of which venue to submit a new paper, we further tell from our experiments results that different authors have different influence over the final choice of venues.","abstract_html":"When a new paper is completed, choosing a good conference or journal in which to publish this new paper is of critical importance to all researchers. Authors often make their decision based on the topics suitability between the paper content and target venues, the likelihood of getting accepted into the venues, the publication history of the authors and other reasonable considerations. A good number of works do content-based analysis to match the topics of the paper and target venues. Such approaches often use full texts, abstracts along with other meta data. The main challenge, for this line of works, is to resolve topic ambiguity because many venues share similar topics and topics evolve over time. Another line of works are network-based approaches, which make recommendations using co-author networks and author-venue links in the bibliographic information networks. However, we have not yet seen a general framework that incorporates a broad range of both content-based features and network-based features, which are potentially capable of delivering more information to help solve the problem. In this thesis, we propose a general framework to automatically find appropriate venues for a new paper using a heterogeneous information network approach. First, meta path-based topological features are systematically extracted from the underlying bibliographic network. Then, a supervised model is used to learn the weights associated with different topological features in deciding the most suitable venues. Experiments on Microsoft Academic Graph(MAG) datasets show that our new approach consistently outperforms existing works by venue prediction accuracy. Results also show that not only topics information but authors' networks and publication history are important factors in the the problem of which venue to submit a new paper, we further tell from our experiments results that different authors have different influence over the final choice of venues.","abstract_has_math":false,"creators":["Cai, Haoyan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Han, Jiawei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-07T20:28:01Z","date_published":"2016-07-07T20:28:01Z","updated_at":"2026-07-22T22:26:34Z","subjects":["venue recommendation","heterogeneous information networks","meta paths"],"languages":["en"],"rights":["Copyright 2016 Haoyan Cai"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90825","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei"]},{"key":"dc:creator","label":"Author","values":["Cai, Haoyan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T20:28:01Z","2018-07-08T09:15:23Z","2016-04-25","2016-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["venue recommendation","heterogeneous information networks","meta paths"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Haoyan Cai"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90825"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["When a new paper is completed, choosing a good conference or journal in which to publish this new paper is of critical importance to all researchers. Authors often make their decision based on the topics suitability between the paper content and target venues, the likelihood of getting accepted into the venues, the publication history of the authors and other reasonable considerations. A good number of works do content-based analysis to match the topics of the paper and target venues. Such approaches often use full texts, abstracts along with other meta data. The main challenge, for this line of works, is to resolve topic ambiguity because many venues share similar topics and topics evolve over time. Another line of works are network-based approaches, which make recommendations using co-author networks and author-venue links in the bibliographic information networks. However, we have not yet seen a general framework that incorporates a broad range of both content-based features and network-based features, which are potentially capable of delivering more information to help solve the problem. In this thesis, we propose a general framework to automatically find appropriate venues for a new paper using a heterogeneous information network approach. First, meta path-based topological features are systematically extracted from the underlying bibliographic network. Then, a supervised model is used to learn the weights associated with different topological features in deciding the most suitable venues. Experiments on Microsoft Academic Graph(MAG) datasets show that our new approach consistently outperforms existing works by venue prediction accuracy. Results also show that not only topics information but authors' networks and publication history are important factors in the the problem of which venue to submit a new paper, we further tell from our experiments results that different authors have different influence over the final choice of venues.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Haoyan Cai, accepted the attached license on 2016-04-25 at 15:13.","The student, Haoyan Cai, submitted this Thesis for approval on 2016-04-25 at 15:30.","This Thesis was approved for publication on 2016-04-25 at 17:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9483 on 2016-07-07 at 13:50:50","Made available in DSpace on 2016-07-07T20:28:01Z (GMT). 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Authors often make their decision based on the topics suitability between the paper content and target venues, the likelihood of getting accepted into the venues, the publication history of the authors and other reasonable considerations. A good number of works do content-based analysis to match the topics of the paper and target venues. Such approaches often use full texts, abstracts along with other meta data. The main challenge, for this line of works, is to resolve topic ambiguity because many venues share similar topics and topics evolve over time. Another line of works are network-based approaches, which make recommendations using co-author networks and author-venue links in the bibliographic information networks. However, we have not yet seen a general framework that incorporates a broad range of both content-based features and network-based features, which are potentially capable of delivering more information to help solve the problem. In this thesis, we propose a general framework to automatically find appropriate venues for a new paper using a heterogeneous information network approach. First, meta path-based topological features are systematically extracted from the underlying bibliographic network. Then, a supervised model is used to learn the weights associated with different topological features in deciding the most suitable venues. Experiments on Microsoft Academic Graph(MAG) datasets show that our new approach consistently outperforms existing works by venue prediction accuracy. Results also show that not only topics information but authors' networks and publication history are important factors in the the problem of which venue to submit a new paper, we further tell from our experiments results that different authors have different influence over the final choice of venues.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Haoyan Cai, accepted the attached license on 2016-04-25 at 15:13.","The student, Haoyan Cai, submitted this Thesis for approval on 2016-04-25 at 15:30.","This Thesis was approved for publication on 2016-04-25 at 17:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9483 on 2016-07-07 at 13:50:50","Made available in DSpace on 2016-07-07T20:28:01Z (GMT). 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