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

TL;DR

This paper introduces CoHeat, a novel method for cold-start bundle recommendation that leverages popularity-based coalescence, graph-based representations, and curriculum and contrastive learning to improve accuracy significantly.

Contribution

The paper presents CoHeat, a new approach that effectively addresses the cold-start bundle recommendation problem by integrating popularity-based coalescence with advanced learning techniques.

Findings

Achieves up to 193% higher nDCG@20 than competitors.

Effectively models user-bundle relationships with graph-based views.

Utilizes curriculum and contrastive learning for better latent representations.

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.