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Context-Aware Preference Learning System Based on Dirichlet Process Gaussian Mixture Model
We study a context-aware preference learning system that automatically learns user preferences in different environments. The system is based on a Dirichlet process Gaussian mixture model and comprises an environmental model and a preference learning system. The environmental model is used to determ...
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creator | Xu, Xianbo van Erp, Bart Ignatenko, Tanya |
description | We study a context-aware preference learning system that automatically learns user preferences in different environments. The system is based on a Dirichlet process Gaussian mixture model and comprises an environmental model and a preference learning system. The environmental model is used to determine the user's current environment, based on environmental signals. The preference learning system actively learns user preferences through pairwise comparison trials. During these trials, the so-called reference and alternative proposals are generated by the system and presented to a user, who, in his/her turn, indicates the preferred one. We propose a novel inference approach that allows for environmental model per-sonalization and leads to improved system performance. This is achieved by introducing a two-way dependency between environment and preference domains. Our approach is validated through simulations that demonstrate a significant performance improvement with respect to the baseline model. |
doi_str_mv | 10.1109/ICASSP48485.2024.10448188 |
format | conference_proceeding |
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Our approach is validated through simulations that demonstrate a significant performance improvement with respect to the baseline model.</description><subject>Acoustics</subject><subject>Active learning</subject><subject>Bayesian nonparametric modelling</subject><subject>clustering</subject><subject>Dirichlet process</subject><subject>Gaussian mixture model</subject><subject>Learning systems</subject><subject>preference learning</subject><subject>Proposals</subject><subject>Signal processing</subject><subject>Speech processing</subject><subject>System performance</subject><issn>2379-190X</issn><isbn>9798350344851</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1kM1KAzEUhaMgWGvfwEV8gKk3_8myVluFFgvtQtyUzMyNRtoZSabYvr0D6urA4ePjcAi5ZTBmDNzd83SyXq-klVaNOXA5ZiClZdaekZEzzgoFoi8UOycDLowrmIPXS3KV8ycAWCPtgLxN26bDY1dMvn1CukoYMGFTIV2gT01s3un6lDvc03ufsaZtQx9iitXHDruebivMmc79IefoG7qMx-7Qa5ZtjbtrchH8LuPoL4dkM3vcTJ-Kxcu8n74oouG2cKXm1rDgapDBhAq1ZmhDabQulQLQwUmuRemZkExhxbgStew5iyBKa8SQ3PxqIyJuv1Lc-3Ta_l8hfgAzzlRW</recordid><startdate>20240414</startdate><enddate>20240414</enddate><creator>Xu, Xianbo</creator><creator>van Erp, Bart</creator><creator>Ignatenko, Tanya</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20240414</creationdate><title>Context-Aware Preference Learning System Based on Dirichlet Process Gaussian Mixture Model</title><author>Xu, Xianbo ; van Erp, Bart ; Ignatenko, Tanya</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i728-9b62871f9d04f7fce661e8fb766b55006f94263ba13415ec1253d4fce8e03b873</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Acoustics</topic><topic>Active learning</topic><topic>Bayesian nonparametric modelling</topic><topic>clustering</topic><topic>Dirichlet process</topic><topic>Gaussian mixture model</topic><topic>Learning systems</topic><topic>preference learning</topic><topic>Proposals</topic><topic>Signal processing</topic><topic>Speech processing</topic><topic>System performance</topic><toplevel>online_resources</toplevel><creatorcontrib>Xu, Xianbo</creatorcontrib><creatorcontrib>van Erp, Bart</creatorcontrib><creatorcontrib>Ignatenko, Tanya</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library Online</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xu, Xianbo</au><au>van Erp, Bart</au><au>Ignatenko, Tanya</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Context-Aware Preference Learning System Based on Dirichlet Process Gaussian Mixture Model</atitle><btitle>ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</btitle><stitle>ICASSP</stitle><date>2024-04-14</date><risdate>2024</risdate><spage>6805</spage><epage>6809</epage><pages>6805-6809</pages><eissn>2379-190X</eissn><eisbn>9798350344851</eisbn><abstract>We study a context-aware preference learning system that automatically learns user preferences in different environments. 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identifier | EISSN: 2379-190X |
ispartof | ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024, p.6805-6809 |
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subjects | Acoustics Active learning Bayesian nonparametric modelling clustering Dirichlet process Gaussian mixture model Learning systems preference learning Proposals Signal processing Speech processing System performance |
title | Context-Aware Preference Learning System Based on Dirichlet Process Gaussian Mixture Model |
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