{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108339"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108339","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Recommendations in text-rich heterogeneous networks","abstract":"In this work, we study the problem of performing recommendations in a Heterogeneous Network which has auxiliary text information present with the nodes. We cover the relevant background and illustrate heterogeneous networks along with related tasks through the help of examples. We choose the setting of Bibliographic Heterogeneous Network and devise a Citation Recommendation system that integrates the various sources of information present in the network. We utilize specific similarity matrices to compare the query paper with the set of candidate papers which enables us to capture the query-specific context of the candidate papers. Our proposed approach employs suitably transformed embeddings to create the similarity matrices and follows-up with convolution neural networks. We demonstrate the effectiveness of our solution over two popular datasets where our method outperforms several network and/or text-based methods. We also perform a thorough qualitative analysis based on sample queries to show the effectiveness of our model in holistically combining heterogeneous information.","abstract_html":"In this work, we study the problem of performing recommendations in a Heterogeneous Network which has auxiliary text information present with the nodes. We cover the relevant background and illustrate heterogeneous networks along with related tasks through the help of examples. We choose the setting of Bibliographic Heterogeneous Network and devise a Citation Recommendation system that integrates the various sources of information present in the network. We utilize specific similarity matrices to compare the query paper with the set of candidate papers which enables us to capture the query-specific context of the candidate papers. Our proposed approach employs suitably transformed embeddings to create the similarity matrices and follows-up with convolution neural networks. We demonstrate the effectiveness of our solution over two popular datasets where our method outperforms several network and/or text-based methods. We also perform a thorough qualitative analysis based on sample queries to show the effectiveness of our model in holistically combining heterogeneous information.","abstract_has_math":false,"creators":["Raj, Jeetu"],"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":2020,"date_issued":"2020-08-27T00:51:31Z","date_published":"2020-08-27T00:51:31Z","updated_at":"2026-07-22T22:24:48Z","subjects":["Network Mining","Recommendation","Deep Learning","Heterogeneous Networks","Text-Rich Networks"],"languages":["en"],"rights":["Copyright 2020 Jeetu Raj"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108339","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":["Raj, Jeetu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-27T00:51:31Z","2022-08-27T00:51:40Z","2020-05-11","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["Network Mining","Recommendation","Deep Learning","Heterogeneous Networks","Text-Rich Networks"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Jeetu Raj"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108339"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this work, we study the problem of performing recommendations in a Heterogeneous Network which has auxiliary text information present with the nodes. We cover the relevant background and illustrate heterogeneous networks along with related tasks through the help of examples. We choose the setting of Bibliographic Heterogeneous Network and devise a Citation Recommendation system that integrates the various sources of information present in the network. We utilize specific similarity matrices to compare the query paper with the set of candidate papers which enables us to capture the query-specific context of the candidate papers. Our proposed approach employs suitably transformed embeddings to create the similarity matrices and follows-up with convolution neural networks. We demonstrate the effectiveness of our solution over two popular datasets where our method outperforms several network and/or text-based methods. We also perform a thorough qualitative analysis based on sample queries to show the effectiveness of our model in holistically combining heterogeneous information.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-05-01","The student, Jeetu Raj, accepted the attached license on 2020-05-08 at 13:41.","The student, Jeetu Raj, submitted this Thesis for approval on 2020-05-09 at 12:28.","This Thesis was approved for publication on 2020-05-11 at 13:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15296 on 2020-08-25 at 17:44:15","Made available in DSpace on 2020-08-27T00:51:31Z (GMT). No. of bitstreams: 2 RAJ-THESIS-2020.pdf: 615796 bytes, checksum: 35dd8c813bd2bdeef85a426778d43741 (MD5) LICENSE.txt: 4206 bytes, checksum: 6b04ff9089a504d2ea58e6dd9c63846c (MD5) Previous issue date: 2020-05-11","Embargo set by: Seth Robbins for item 115954 Lift date: 2022-08-27T00:51:40Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Recommendations in text-rich heterogeneous networks"]}]}],"canonical_facts":{"dc:contributor":["Han, Jiawei"],"dc:creator":["Raj, Jeetu"],"dc:date":["2020-08-27T00:51:31Z","2022-08-27T00:51:40Z","2020-05-11","2020-05"],"dc:description":["In this work, we study the problem of performing recommendations in a Heterogeneous Network which has auxiliary text information present with the nodes. We cover the relevant background and illustrate heterogeneous networks along with related tasks through the help of examples. We choose the setting of Bibliographic Heterogeneous Network and devise a Citation Recommendation system that integrates the various sources of information present in the network. We utilize specific similarity matrices to compare the query paper with the set of candidate papers which enables us to capture the query-specific context of the candidate papers. Our proposed approach employs suitably transformed embeddings to create the similarity matrices and follows-up with convolution neural networks. We demonstrate the effectiveness of our solution over two popular datasets where our method outperforms several network and/or text-based methods. We also perform a thorough qualitative analysis based on sample queries to show the effectiveness of our model in holistically combining heterogeneous information.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-05-01","The student, Jeetu Raj, accepted the attached license on 2020-05-08 at 13:41.","The student, Jeetu Raj, submitted this Thesis for approval on 2020-05-09 at 12:28.","This Thesis was approved for publication on 2020-05-11 at 13:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15296 on 2020-08-25 at 17:44:15","Made available in DSpace on 2020-08-27T00:51:31Z (GMT). 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