How can we accurately recommend cold-start bundles to users? The cold-start problem in bundle recommendation is critical in practical scenarios since new bundles are continuously created for various marketing purposes. Despite its importance, no previous studies have addressed cold-start bundle recommendation. Moreover, existing methods for cold-start item recommendation overly rely on historical information, even for unpopular bundles, failing to tackle 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 the cold-start bundle recommendation. CoHeat 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, CoHeat 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.

翻译：如何向用户精准推荐冷启动捆绑包？在实际场景中，由于新捆绑包因各类营销目的不断生成，捆绑推荐中的冷启动问题至关重要。尽管其重要性显著，但此前尚无研究专门解决冷启动捆绑推荐问题。此外，现有的冷启动物品推荐方法过度依赖历史信息（即使对冷门捆绑包也是如此），未能有效应对捆绑包交互数据高度偏态分布的核心挑战。本文提出CoHeat方法（基于流行度的聚合与课程加热），一种针对冷启动捆绑推荐的精准方案。CoHeat通过结合捆绑包流行度相关的历史信息与关联信息来估计用户-捆绑包关系，从而解决交互数据的高度偏态分布问题；同时，该方法利用课程学习与对比学习高效学习潜在表征。实验表明，CoHeat在冷启动捆绑推荐中表现卓越，其nDCG@20指标较最优基线方法提升高达193%。