Accurate Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating

Hyunsik Jeon, Jong-eun Lee, Jeongin Yun, U Kang

Keywords: cold-start bundle recommendation, popularity-based coalescence, curriculum heating, contrastive learning

TL;DR: We address the cold-start bundle recommendation and achieve the state-of-the-art performance by proposing popularity-based coalescence and curriculum heating.

Abstract: How can we accurately recommend cold-start bundles to users? The cold-start problem in bundle recommendation is crucial in practical scenarios since 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. It then tackles the highly skewed distribution of bundle interactions by incorporating both historical and affiliation information based on the bundle's popularity when estimating the user-bundle relationship. 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.

Track: User Modeling and Recommendation

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Submission Number: 372