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DEEP LEARNING BASED VISUAL COMPATIBILITY PREDICTION FOR BUNDLE RECOMMENDATIONS

机译:基于深度学习的捆绑建议的可视兼容性预测

摘要

Embodiments of the present invention provide systems, methods, and computer storage media for predicting visual compatibility between a bundle of catalog items (e.g., a partial outfit) and a candidate catalog item to add to the bundle. Visual compatibility prediction may be jointly conditioned on item type, context, and style by determining a first compatibility score jointly conditioned on type (e.g., category) and context, determining a second compatibility score conditioned on outfit style, and combining the first and second compatibility scores into a unified visual compatibility score. A unified visual compatibility score may be determined for each of a plurality of candidate items, and the candidate item with the highest unified visual compatibility score may be selected to add to the bundle (e.g., fill the in blank for the partial outfit).
机译:本发明的实施例提供了用于预测一束目录项(例如,部分装备)和候选目录项之间的可视兼容性的系统,方法和计算机存储介质。 可以通过确定类型(例如,类别)和上下文的第一个兼容性分数来共同调节可视兼容性预测,通过确定类型(例如,类别)和上下文,确定在装备样式上调节的第二兼容性分数,并组合第一和第二兼容性 分为统一的视觉兼容性分数。 可以针对多个候选项目中的每一个确定统一的视觉兼容性分数,并且可以选择具有最高统一视觉兼容性分数的候选项目来添加到束(例如,填充部分套装的空白)。

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