Learning multi-faceted prototypical user interests

We seek to uncover the latent interest units from behavioral data to better learn user preferences under the VAE framework. Existing practices tend to ignore the multiple facets of item characteristics, which may not capture it at appropriate granularity. Moreover, current studies equate the granula...

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Main Authors: TRAN, Nhu Thuat, LAUW, Hady W.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9281
https://ink.library.smu.edu.sg/context/sis_research/article/10281/viewcontent/5185_Learning_Multi_Faceted_Pr.pdf
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spelling sg-smu-ink.sis_research-102812024-10-17T07:54:44Z Learning multi-faceted prototypical user interests TRAN, Nhu Thuat LAUW, Hady W. We seek to uncover the latent interest units from behavioral data to better learn user preferences under the VAE framework. Existing practices tend to ignore the multiple facets of item characteristics, which may not capture it at appropriate granularity. Moreover, current studies equate the granularity of item space to that of user interests, which we postulate is not ideal as user interests would likely map to a small subset of item space. In addition, the compositionality of user interests has received inadequate attention, preventing the modeling of interactions between explanatory factors driving a user's decision. To resolve this, we propose to align user interests with multi-faceted item characteristics. First, we involve prototype-based representation learning to discover item characteristics along multiple facets. Second, we compose user interests from uncovered item characteristics via binding mechanism, separating the granularity of user preferences from that of item space. Third, we design a dedicated bi-directional binding block, aiding the derivation of compositional user interests. On real-world datasets, the experimental results demonstrate the strong performance of our proposed method compared to a series of baselines. 2024-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9281 https://ink.library.smu.edu.sg/context/sis_research/article/10281/viewcontent/5185_Learning_Multi_Faceted_Pr.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University multi-faceted representation user interests item characteristics Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic multi-faceted representation
user interests
item characteristics
Databases and Information Systems
spellingShingle multi-faceted representation
user interests
item characteristics
Databases and Information Systems
TRAN, Nhu Thuat
LAUW, Hady W.
Learning multi-faceted prototypical user interests
description We seek to uncover the latent interest units from behavioral data to better learn user preferences under the VAE framework. Existing practices tend to ignore the multiple facets of item characteristics, which may not capture it at appropriate granularity. Moreover, current studies equate the granularity of item space to that of user interests, which we postulate is not ideal as user interests would likely map to a small subset of item space. In addition, the compositionality of user interests has received inadequate attention, preventing the modeling of interactions between explanatory factors driving a user's decision. To resolve this, we propose to align user interests with multi-faceted item characteristics. First, we involve prototype-based representation learning to discover item characteristics along multiple facets. Second, we compose user interests from uncovered item characteristics via binding mechanism, separating the granularity of user preferences from that of item space. Third, we design a dedicated bi-directional binding block, aiding the derivation of compositional user interests. On real-world datasets, the experimental results demonstrate the strong performance of our proposed method compared to a series of baselines.
format text
author TRAN, Nhu Thuat
LAUW, Hady W.
author_facet TRAN, Nhu Thuat
LAUW, Hady W.
author_sort TRAN, Nhu Thuat
title Learning multi-faceted prototypical user interests
title_short Learning multi-faceted prototypical user interests
title_full Learning multi-faceted prototypical user interests
title_fullStr Learning multi-faceted prototypical user interests
title_full_unstemmed Learning multi-faceted prototypical user interests
title_sort learning multi-faceted prototypical user interests
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9281
https://ink.library.smu.edu.sg/context/sis_research/article/10281/viewcontent/5185_Learning_Multi_Faceted_Pr.pdf
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