Cold-start Bundle Recommendation via Popularity-based Coalescence and
Curriculum Heating
- URL: http://arxiv.org/abs/2310.03813v3
- Date: Sun, 10 Mar 2024 05:13:41 GMT
- Title: Cold-start Bundle Recommendation via Popularity-based Coalescence and
Curriculum Heating
- Authors: Hyunsik Jeon, Jong-eun Lee, Jeongin Yun, U Kang
- Abstract summary: Existing methods for cold-start item recommendation are not readily applicable to bundles.
We propose CoHeat, an accurate approach for cold-start bundle recommendation.
CoHeat demonstrates superior performance in cold-start bundle recommendation, achieving up to 193% higher nDCG@20 compared to the best competitor.
- Score: 16.00757636715368
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: How can we recommend cold-start bundles to users? The cold-start problem in
bundle recommendation is crucial because new bundles are continuously created
on the Web for various marketing purposes. Despite its importance, existing
methods for cold-start item recommendation are not readily applicable to
bundles. They depend overly on historical information, even for less popular
bundles, failing to address the primary challenge of the highly skewed
distribution of bundle interactions. In this work, we propose CoHeat
(Popularity-based Coalescence and Curriculum Heating), an accurate approach for
cold-start bundle recommendation. CoHeat first represents users and bundles
through graph-based views, capturing collaborative information effectively. To
estimate the user-bundle relationship more accurately, CoHeat addresses the
highly skewed distribution of bundle interactions through a popularity-based
coalescence approach, which incorporates historical and affiliation information
based on the bundle's popularity. Furthermore, it effectively learns latent
representations by exploiting curriculum learning and contrastive learning.
CoHeat demonstrates superior performance in cold-start bundle recommendation,
achieving up to 193% higher nDCG@20 compared to the best competitor.
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