Ranking Search Results Based on Lookalike Users on Online Social Networks

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

• receive, from a client system of the first user, a search query comprising a plurality of n-grams inputted by the first user;
• identify a plurality of content objects associated with the online social network that match the plurality of n-grams;
• access lookalike data of one or more lookalike users with respect to the first user, wherein the first user corresponds to a first user-vector, the one or more lookalike users being selected from a plurality of second users of the online social network, the plurality of second users corresponding to a plurality of second user-vectors, respectively, wherein each user-vector is an N-dimensional vector representing the respective user in an N-dimensional vector space, each dimension of the user-vector corresponding to a social-networking trait of the respective user, and wherein each second user is selected based on a vector similarity between the first user-vector and the second-user vector corresponding to the respective second user;
• calculate, by a machine-learning model associated with the first user, a relevancy score for each of the identified content objects, wherein the relevancy score is based on one or more prior interactions of one or more of the lookalike users with content objects associated with the online social network;
• rank the plurality of identified content objects at least in part based on the relevancy score of the identified content object;
• and
• send, to the client system of the first user for display, a search-results interface comprising one or more search results corresponding to one or more of the identified content objects, the search result being presented in ranked order based on the ranking of the respective identified content object.

A system comprising:

• one or more processors;
• and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to:
• receive, from a client system of the first user, a search query comprising a plurality of n-grams inputted by the first user;
• identify a plurality of content objects associated with the online social network that match the plurality of n-grams;
• access lookalike data of one or more lookalike users with respect to the first user, wherein the first user corresponds to a first user-vector, the one or more lookalike users being selected from a plurality of second users of the online social network, the plurality of second users corresponding to a plurality of second user-vectors, respectively, wherein each user-vector is an N-dimensional vector representing the respective user in an N-dimensional vector space, each dimension of the user-vector corresponding to a social-networking trait of the respective user, and wherein each second user is selected based on a vector similarity between the first user-vector and the second-user vector corresponding to the respective second user;
• calculate, by a machine-learning model associated with the first user, a relevancy score for each of the identified content objects, wherein the relevancy score is based on one or more prior interactions of one or more of the lookalike users with content objects associated with the online social network;
• rank the plurality of identified content objects at least in part based on the relevancy score of the identified content object;
• and
• send, to the client system of the first user for display, a search-results interface comprising one or more search results corresponding to one or more of the identified content objects, the search result being presented in ranked order based on the ranking of the respective identified content object.