TECHNIQUES FOR IMPROVING DOWNSTREAM UTILITY IN MAKING FOLLOW RECOMMENDATIONS

摘要

Described herein is a technique to generate and present follow recommendations. During a first stage or phase, training data are obtained by presenting follow recommendations to some randomly selected set of members, and then observing the collective members' responses. Using the training data, first and second predictive machine-learned scoring models are derived—the first scoring model for use in predicting when a member will opt to follow an entity being recommended, and the second scoring model for use in predicting if the member will engage with content presented via a newly formed follow edge. Then, using the scoring models, follow recommendations are derived, scored, and ultimately selected—based on their scores—for presentation to a member.