Creating a taxonomy of interests is expensive and human-effort intensive: not only do we need to identify nodes and interconnect them, in order to use the taxonomy, we must also connect the nodes to relevant entities such as users, pins, and queries. Connecting to entities is challenging because of ambiguities inherent to language but also because individual interests are dynamic and evolve. Here, we offer an alternative approach that begins with bottom-up discovery of $\mu$-topics called pincepts. The discovery process itself connects these $\mu$-topics dynamically with relevant queries, pins, and users at high precision, automatically adapting to shifting interests. Pincepts cover all areas of user interest and automatically adjust to the specificity of user interests and are thus suitable for the creation of various kinds of taxonomies. Human experts associate taxonomy nodes with $\mu$-topics (on average, 3 $\mu$-topics per node), and the $\mu$-topics offer a high-level data layer that allows quick definition, immediate inspection, and easy modification. Even more powerfully, $\mu$-topics allow easy exploration of nearby semantic space, enabling curators to spot and fill gaps. Curators' domain knowledge is heavily leveraged and we thus don't need untrained mechanical Turks, allowing further cost reduction. These $\mu$-topics thus offer a satisfactory "symbolic" stratum over which to define taxonomies. We have successfully applied this technique for very rapidly iterating on and launching the home decor and fashion styles taxonomy for style-based personalization, prominently featured at the top of Pinterest search results, at 94% precision, improving search success rate by 34.8% as well as boosting long clicks and pin saves.

翻译：构建兴趣分类体系成本高昂且需要大量人力投入：不仅需要识别节点并建立其关联，为了使用分类体系，还必须将节点与用户、图钉和查询等相关实体连接。由于语言本身存在歧义，且个体兴趣具有动态演变特性，连接实体面临诸多挑战。本文提出一种替代方案，通过自底向上发现称为"图钉概念"的$\mu$-主题来构建分类体系。发现过程本身以高精度动态连接这些$\mu$-主题与相关查询、图钉和用户，并自动适应兴趣变化。图钉概念覆盖用户兴趣的所有领域，自动适配到用户兴趣的细粒度，因此适用于构建各类分类体系。人类专家将分类体系节点与$\mu$-主题关联（平均每个节点对应3个$\mu$-主题），这些$\mu$-主题提供高层数据层，支持快速定义、即时检验和便捷修改。更强大的是，$\mu$-主题允许轻松探索邻近语义空间，使策划者能够发现并填补空白。由于充分利用策划者领域知识，无需未经训练的机械土耳其人，进一步降低成本。这些$\mu$-主题为定义分类体系提供了令人满意的"符号"层。我们已成功将该技术应用于家居装饰和时尚风格分类体系的快速迭代与发布，用于基于风格个性化推荐，在Pinterest搜索结果顶部显著位置以94%精度展示，使搜索成功率提升34.8%，同时增加长点击和图钉保存量。