System and method for detecting visually similar items

A computer-implemented method for image-based analysis, the method comprising:

• determining a set of target listings of an online e-commerce system, each target listing of the set of target listings includes a category identifier, a listing identifier, and an image associated with an item;
• for a seed listing of the set of target listings, retrieving a seed image of a seed item associated with the seed listing, the seed listing categorized within a first item category;
• training a convolutional neural network (CNN) with images of items, the images of items being labeled with the first item category;
• generating a seed item feature vector for the seed image using the CNN trained with the images of items labeled with the first item category, wherein the seed image is an input to the CNN, the seed item feature vector including an array of values representing features of the image;
• identifying a plurality of feature vectors associated with the first item category, wherein each feature vector of the plurality of feature vectors is associated with a listing categorized within the first item category, the plurality of feature vectors being previously stored;
• adding the seed item feature vector to the stored plurality of feature vectors;
• comparing the seed item feature vector to the plurality of feature vectors using a k-nearest neighbors (kNN) algorithm;
• generating a first set of nearest neighbor listings to the seed listing based on the comparing and based on a time that the seed listing was added to the online e-commerce system, wherein each nearest neighbor listing represents a listing for an item that is visually similar to the seed item and that is categorized within the first item category;
• storing the first set of nearest neighbor listings associated with the seed listing in a database;
• receiving, via a client device subsequent to storing the first set of nearest neighbor listings in the database, a query for the seed listing;
• selecting one or more nearest neighbor listings from the first set of nearest neighbor listings stored in the database based on receiving the query;
• communicating the one or more nearest neighbor listings to the client device as a recommendation of an online e-commerce system in response to the query;
• and
• generating a second set of nearest neighbor listings to the seed listing based on a passage of a time duration following generation of the first set of nearest neighbor listings, wherein the second set of nearest neighbor listings include at least one listing for an item that was added to the online e-commerce system following generation of the first set of nearest neighbor listings.

A non-transitory machine-readable medium storing processor-executable instructions which, when executed by a processor, cause a system to:

• determine a set of target listings of an online e-commerce system, each target listing of the set of target listings includes a category identifier, a listing identifier, and an image associated with an item;
• for a seed listing of the set of target listings, retrieve a seed image of a seed item associated with the seed listing, the seed listing categorized within a first item category;
• train a convolutional neural network (CNN) with images of items, the images of items being labeled with the first item category;
• generate a seed item feature vector for the seed image using the CNN trained with the images of items labeled with the first item category, wherein the seed image is an input to the CNN, the seed item feature vector including an array of values representing features of the image;
• identify a plurality of feature vectors associated with the first item category, each feature vector of the plurality of feature vectors is associated with a listing categorized within the first item category, the plurality of feature vectors being previously stored;
• add the seed item feature vector to the stored plurality of feature vectors;
• compare the seed item feature vector to the plurality of feature vectors using a k-nearest neighbors (kNN) algorithm;
• generate a first set of nearest neighbor listings to the seed listing based on the comparing and based on a time that the seed listing was added to the online e-commerce system, wherein each nearest neighbor listing represents a listing for an item that is visually similar to the seed item and that is categorized within the first item category;
• store the first set of nearest neighbor listings associated with the seed listing in a database;
• receive, via a client device subsequent to storing the first set of nearest neighbor listings in the database, a query for the seed listing;
• select one or more nearest neighbor listings from the first set of nearest neighbor listings stored in the database based on receiving the query;
• communicate the one or more nearest neighbor listings to a client device as a recommendation of an online e-commerce system in response to the query;
• and
• generate a second set of nearest neighbor listings to the seed listing based on a passage of a time duration following generation of the first set of nearest neighbor listings, wherein the second set of nearest neighbor listings include at least one listing for an item that was added to the online e-commerce system following generation of the first set of nearest neighbor listings.