Deduplicating recommendations to a user of an online system using a computer model and clustering

A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising:

• clustering, based at least in part on a similarity score for each pair of items of a plurality of items previously purchased by a user of an online system, the plurality of items into a plurality of clusters so that each of the plurality of clusters includes a respective subset of the plurality of items that are more similar to each other than to any other item clustered to any of remaining clusters of the plurality of clusters;
• accessing a computer model of the online system trained to predict a likelihood of engagement by the user for each item in each of the plurality of clusters;
• applying the computer model to predict, based at least in part on one or more features of each item in each of the plurality of clusters, the likelihood of engagement for each item in each of the plurality of clusters;
• generating, based at least in part on the predicted likelihood of engagement, a score for each item in each of the plurality of clusters;
• selecting, based at least in part on the score for each item in each of the plurality of clusters, one or more representative items from each of the plurality of clusters;
• causing a device associated with the user to display a user interface with the one or more representative items from each of the plurality of clusters;
• and
• updating, based at least in part on feedback from the user in relation to the one or more representative items, a set of parameters of the computer model.

A computer system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps comprising:
• clustering, based at least in part on a similarity score for each pair of items of a plurality of items previously purchased by a user of an online system, the plurality of items into a plurality of clusters so that each of the plurality of clusters includes a respective subset of the plurality of items that are more similar to each other than to any other item clustered to any of remaining clusters of the plurality of clusters;
• accessing a computer model of the online system trained to predict a likelihood of engagement by the user for each item in each of the plurality of clusters;
• applying the computer model to predict, based at least in part on one or more features of each item in each of the plurality of clusters, the likelihood of engagement for each item in each of the plurality of clusters;
• generating, based at least in part on the predicted likelihood of engagement, a score for each item in each of the plurality of clusters;
• selecting, based at least in part on the score for each item in each of the plurality of clusters, one or more representative items from each of the plurality of clusters;
• causing a device associated with the user to display a user interface with the one or more representative items from each of the plurality of clusters;
• and
• updating, based at least in part on feedback from the user in relation to the one or more representative items, a set of parameters of the computer model.