Snap Inc.

Ding Zhou

Jason Brewer

Yiwei Ma

Manish Malik

Mingtao Zhang

Lili Zhang

Yinan Na

Chang Kuang Huang

Dan Xie

Jinchao Ye

Hangqi Zhao

Shuo Han

Yang Zhou

James Li

Yining Zhang

Techniques for creating an interest graph include obtaining content items from multiple content sources and applying tailored (e.g., source-specific) preprocessing to the content items based on their respective content source. Text is extracted and salient keywords and key phrases are identified using unsupervised machine learning models. The keywords and key phrases become nodes in an interest graph, each node comprising an embedding of a keyword or key phrase in a common embedding space, with edges representing semantic similarity based on embeddings or co-engagement patterns. The graph provides an expansive, granular, and dynamic taxonomy easily adaptable to emerging interests. The interest graph overcomes limitations of conventional taxonomies that lack depth, fail to capture niche interests, and cannot adapt to reflect evolving user preferences. The described techniques construct a rich interest graph from diverse content for improved content understanding.

Automatic techniques for constructing an evolving interest taxonomy from user-generated content

Growing community of inventors

Los Altos Hills, CA, United States of America

Ding Zhou