{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113920"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113920","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Demystifying graph neural networks in recommender systems","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","abstract_has_math":false,"creators":["Ji, Houxiang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Torrellas, Josep"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-04-29T21:34:54Z","date_published":"2022-04-29T21:34:54Z","updated_at":"2026-07-22T22:24:53Z","subjects":["Computer science"],"languages":["en","eng"],"rights":["Copyright 2021 Houxiang Ji"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113920","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Torrellas, Josep"]},{"key":"dc:creator","label":"Author","values":["Ji, Houxiang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-04-29T21:34:54Z","2021-12","2021-12-06"]},{"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":["Computer science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Houxiang Ji"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113920"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","The student, Houxiang Ji, accepted the attached license on 2021-12-06 at 13:01.","The student, Houxiang Ji, submitted this Thesis for approval on 2021-12-06 at 13:03.","This Thesis was approved for publication on 2021-12-06 at 16:38.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17392 on 2022-04-06 at 17:11:08","Made available in DSpace on 2022-04-29T21:34:54Z (GMT). No. of bitstreams: 2 JI-THESIS-2021.pdf: 1522994 bytes, checksum: f9b7f43abacdbae1d7a330027be0294c (MD5) LICENSE.txt: 4208 bytes, checksum: 59e30904b848b7a3bc93a29f151dd85e (MD5) Previous issue date: 2021-12-06","Recommender systems have become indispensable tools for many applications with the explosive growth of online information. Recommender systems learn user's interest based on their profile and historical behavior and then recommend the item with the highest predicted ratings. Graph Neural Networks (GNNs) is a powerful graph representation learning method and have shown their unprecedented performance on many scenarios including natural language processing and computer vision. Most data required in the recommender systems is naturally and essentially represented with graph structure, for example, user-item interactions can be represented as a bipartite graph. With the superiority in graph learning, GNNs are gaining more and more attention in the field of recommender systems. This thesis presents our study of GNNs, which are employed in the recommender systems, to characterize their runtime behavior from various levels. To this end, we carefully profile GNN models on training and identify the most expensive operations which are worthy of attention for overhead reduction. We observe that the memory-intensive aggregation phase in the GNNs dominate the overall runtime different from the conventional deep neural network models. Further, we investigate the GNN's behavior at the micro-architectural level, which have received little attention so far, and summarize several key observations. Based on these insightful observations, we propose and discuss software and possible hardware mechanisms to optimize GNN-based recommender systems."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Demystifying graph neural networks in recommender systems"]}]}],"canonical_facts":{"dc:contributor":["Torrellas, Josep"],"dc:creator":["Ji, Houxiang"],"dc:date":["2022-04-29T21:34:54Z","2021-12","2021-12-06"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","The student, Houxiang Ji, accepted the attached license on 2021-12-06 at 13:01.","The student, Houxiang Ji, submitted this Thesis for approval on 2021-12-06 at 13:03.","This Thesis was approved for publication on 2021-12-06 at 16:38.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17392 on 2022-04-06 at 17:11:08","Made available in DSpace on 2022-04-29T21:34:54Z (GMT). No. of bitstreams: 2 JI-THESIS-2021.pdf: 1522994 bytes, checksum: f9b7f43abacdbae1d7a330027be0294c (MD5) LICENSE.txt: 4208 bytes, checksum: 59e30904b848b7a3bc93a29f151dd85e (MD5) Previous issue date: 2021-12-06","Recommender systems have become indispensable tools for many applications with the explosive growth of online information. Recommender systems learn user's interest based on their profile and historical behavior and then recommend the item with the highest predicted ratings. Graph Neural Networks (GNNs) is a powerful graph representation learning method and have shown their unprecedented performance on many scenarios including natural language processing and computer vision. Most data required in the recommender systems is naturally and essentially represented with graph structure, for example, user-item interactions can be represented as a bipartite graph. With the superiority in graph learning, GNNs are gaining more and more attention in the field of recommender systems. This thesis presents our study of GNNs, which are employed in the recommender systems, to characterize their runtime behavior from various levels. To this end, we carefully profile GNN models on training and identify the most expensive operations which are worthy of attention for overhead reduction. We observe that the memory-intensive aggregation phase in the GNNs dominate the overall runtime different from the conventional deep neural network models. Further, we investigate the GNN's behavior at the micro-architectural level, which have received little attention so far, and summarize several key observations. Based on these insightful observations, we propose and discuss software and possible hardware mechanisms to optimize GNN-based recommender systems."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/113920"],"dc:language":["en","eng"],"dc:rights":["Copyright 2021 Houxiang Ji"],"dc:subject":["Computer science"],"dc:title":["Demystifying graph neural networks in recommender systems"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:53Z"}