{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115612"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115612","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Sparsity-aware personalized recommender system via meta-learning","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","abstract_has_math":false,"creators":["Wang, Junting"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Sundaram, Hari"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["Recommendation System","Collaborative Filtering","Meta Learning","Few-shot learning"],"languages":["en","eng"],"rights":["Copyright 2022 Junting Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115612","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sundaram, Hari"]},{"key":"dc:creator","label":"Author","values":["Wang, Junting"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-27"]},{"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":["Recommendation System","Collaborative Filtering","Meta Learning","Few-shot learning"]}]},{"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 2022 Junting Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115612"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","The student, Junting Wang, accepted the attached license on 2022-04-26 at 01:11.","The student, Junting Wang, submitted this Thesis for approval on 2022-04-26 at 01:14.","This Thesis was approved for publication on 2022-04-27 at 13:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17957 on 2022-11-11 at 12:12:06","With the advancement of neural collaborative filtering methods, deep learning methods have become the backbone of modern recommender systems. Neural recommenders provide significant performance gains over conventional methods. However, the recommendations made by them are not truly personalized. Instead, they tend to suggest popular items due to the popularity bias among items. Popularity bias is a fundamental challenge in recommender systems, and it originates from the heavy-tailed distribution of users’ activity data. Modern neural recommenders lack the resolution to rank long-tail items accurately since the interaction data used in the model training is heavily skewed. The biased training results in a biased recommendation model that only recommends the popular subset of the item inventory. In this thesis, we propose a meta-learning framework ProtoCF that effectively eliminates the distribution mismatch between items and learns robust prototype representations for long-tail items. Our experimental results demonstrate that ProtoCF consistently outperforms state-of-the-art approaches on overall recommendation (by 5% Recall@50) while achieving significant gains (of 60-80% Recall@50) for tail items with less than 20 interactions."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Sparsity-aware personalized recommender system via meta-learning"]}]}],"canonical_facts":{"dc:contributor":["Sundaram, Hari"],"dc:creator":["Wang, Junting"],"dc:date":["2022-05","2022-04-27"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","The student, Junting Wang, accepted the attached license on 2022-04-26 at 01:11.","The student, Junting Wang, submitted this Thesis for approval on 2022-04-26 at 01:14.","This Thesis was approved for publication on 2022-04-27 at 13:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17957 on 2022-11-11 at 12:12:06","With the advancement of neural collaborative filtering methods, deep learning methods have become the backbone of modern recommender systems. Neural recommenders provide significant performance gains over conventional methods. However, the recommendations made by them are not truly personalized. Instead, they tend to suggest popular items due to the popularity bias among items. Popularity bias is a fundamental challenge in recommender systems, and it originates from the heavy-tailed distribution of users’ activity data. Modern neural recommenders lack the resolution to rank long-tail items accurately since the interaction data used in the model training is heavily skewed. The biased training results in a biased recommendation model that only recommends the popular subset of the item inventory. In this thesis, we propose a meta-learning framework ProtoCF that effectively eliminates the distribution mismatch between items and learns robust prototype representations for long-tail items. Our experimental results demonstrate that ProtoCF consistently outperforms state-of-the-art approaches on overall recommendation (by 5% Recall@50) while achieving significant gains (of 60-80% Recall@50) for tail items with less than 20 interactions."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115612"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Junting Wang"],"dc:subject":["Recommendation System","Collaborative Filtering","Meta Learning","Few-shot learning"],"dc:title":["Sparsity-aware personalized recommender system via meta-learning"],"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":"2026-07-22T22:24:54Z"}