  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.
