{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/78480"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/78480","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Customized ranking by user preference using LRR model","abstract":"In this thesis, we proposed a customized ranking system that can rank all the entities given a specific user preference. Rank entities by user’s preference is an inevitable strategy of saving user’s time browsing and extracting useful information from Internet. Modern websites always rank these entities by a single numeric value computed by averaging overall rating, but this ranking scheme is of limited use to users. With di↵erent aspect preference, it is obvious that the restaurants ranking should be di↵erent based on their famous features, e.g., service, environment, price. We used the LRR (Latent Rating Regression) model to aggregate restaurants aspect score and proposed two ranking approaches. The experiment results show that the two ranking approaches are both better than the baseline ranking approach.","abstract_html":"In this thesis, we proposed a customized ranking system that can rank all the entities given a specific user preference. Rank entities by user’s preference is an inevitable strategy of saving user’s time browsing and extracting useful information from Internet. Modern websites always rank these entities by a single numeric value computed by averaging overall rating, but this ranking scheme is of limited use to users. With di↵erent aspect preference, it is obvious that the restaurants ranking should be di↵erent based on their famous features, e.g., service, environment, price. We used the LRR (Latent Rating Regression) model to aggregate restaurants aspect score and proposed two ranking approaches. The experiment results show that the two ranking approaches are both better than the baseline ranking approach.","abstract_has_math":false,"creators":["Chiang, Bo-Yu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-07-22T22:17:34Z","date_published":"2015-07-22T22:17:34Z","updated_at":"2026-07-22T22:26:11Z","subjects":["Latent Aspect Rating Analysis (LARA)","Recommendation system"],"languages":["en"],"rights":["Copyright 2015 Bo Yu Chiang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/78480","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Chiang, Bo-Yu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-07-22T22:17:34Z","2015-05","2015-04-24","2015-5"]},{"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":["Latent Aspect Rating Analysis (LARA)","Recommendation system"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Bo Yu Chiang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/78480"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this thesis, we proposed a customized ranking system that can rank all the entities given a specific user preference. 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Modern websites always rank these entities by a single numeric value computed by averaging overall rating, but this ranking scheme is of limited use to users. With di↵erent aspect preference, it is obvious that the restaurants ranking should be di↵erent based on their famous features, e.g., service, environment, price. We used the LRR (Latent Rating Regression) model to aggregate restaurants aspect score and proposed two ranking approaches. The experiment results show that the two ranking approaches are both better than the baseline ranking approach.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2015-07-22 without embargo terms","The student, Bo-Yu Chiang, accepted the attached license on 2015-04-23 at 19:10.","The student, Bo-Yu Chiang, submitted this Thesis for approval on 2015-04-23 at 19:10.","This Thesis was approved for publication on 2015-04-24 at 08:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8070 on 2015-07-22 at 10:33:30","Made available in DSpace on 2015-07-22T22:17:34Z (GMT). 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