MACHINE LEARNING TECHNIQUES TO SHAPE DOWNSTREAM CONTENT TRAFFIC THROUGH HASHTAG SUGGESTION DURING CONTENT CREATION

摘要

Machine learning techniques for shaping downstream content traffic through hashtag suggestion during content creation are provided. In one technique, content item interaction data is stored that indicates, for each of multiple content items that is associated with one or more hashtags, whether a viewer interacted with the content item. Based on the content item interaction data, multiple training instances are generated, each corresponding to a different hashtag. One or more machine learning techniques are used to train a machine-learned downstream interaction model based on the training instances. Based on a particular content item, multiple candidate hashtags are identified. The machine-learned downstream interaction model is used to generate a score for each of the candidate hashtags. A subset of the candidate hashtags is selected based on the scores generated. The subset of the candidate hashtags are caused to be presented on a computing device.