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.

提出了一种名为 CoHeat 的准确的冷启动捆绑推荐方法，能够充分考虑捆绑的流行度和用户捆绑关系的历史和关联信息，并通过课程学习和对比学习有效地学习潜在表示，取得了比竞争方法高达 193% 的水平的冷启动捆绑推荐效果。