Recommender System Based On Trendsetter Inference

A system comprising:

• a user scoring module implemented at least partially in hardware of at least one computing device and configured to generate, for each of multiple users, a trend setting score identifying a degree of trend setting exhibited by the user by identifying a frequency with which the user consumes items that gain popularity after an amount of time, wherein whether an item consumed by the user gains popularity after the amount of time is determined by providing item consumption data for the item to a machine learning system trained to determine based on the item consumption data whether the item became popular the amount of time after the user consumed the item;
• a user affinity module implemented at least partially in hardware of the at least one computing device and configured to determine, for a first user of the multiple users, a second user of the multiple users having characteristic or behavior representations within a threshold distance of characteristic or behavior representations of the first user;
• and
• a trendsetter behavior identification module implemented at least partially in hardware of the at least one computing device and configured to identify a first item that is not popular but has been consumed by the second user for delivery of a recommendation for the first item to the first user via a recommendation delivery platform.

In a digital medium environment to cause an item recommendation to be delivered to a client device, a method implemented by at least one computing device, the method comprising:

• identifying, by the at least one computing device for a first user of multiple users, a set of users including two or more of the multiple users having characteristic or behavior representations within a threshold distance of characteristic or behavior representations of the first user;
• generating, by the at least one computing device and for each user of the set of users, a trend setting score identifying a degree of trend setting exhibited by the user from one or more factors including a frequency with which the user consumes items that gain popularity after an amount of time, a number of first additional users of the multiple users that consumed items after the amount of time, a social media score, and trend setting scores of second additional users of the multiple users that exhibit similar trends after the amount of time, wherein items that gain popularity after an amount of time are items for which a rule indicates the items are popular;
• determining, by the at least one computing device, a second user of the set of users that has a highest trend setting score of the set of users;
• identifying, by the at least one computing device, a first item that is not currently popular but has been consumed by the second user;
• and
• causing, by the at least one computing device, a recommendation for the first item to be communicated over a network for display to the first user.