Shopify Inc.

Putra Manggala

Peng Yu

Xinyi Zhao

Brandon Chu

Jingchuan Zhao

Mladen Rangelov

Methods and systems for managing an online application database and application search. Search queries for applications are received from users. Unfulfilled queries are stored in memory. The platform identifies one or more application features based on the search queries within the stored unfulfilled queries, and generates an application build recommendation specifying the one or more application features. The application build recommendation is output to one or more developer accounts. If a new application is received, the platform may determine whether the new application contains features that sufficiently correspond to the features in one of the application build recommendations. User accounts that submitted the unfulfilled queries that served as the basis for the matching application build recommendation may be notified of the availability of the new application.

Methods and systems for generating application build recommendations

A computer-implemented method and system including a recommendation engine receiving a first user behavior data, the first user behavior data associated with a user action taken by a user, wherein the recommendation engine utilizes a recommendation model to generate recommendations to users based on first user behavior data; labeling the first user behavior; classifying the labeled first user behavior; generating, by the recommendation engine, a recommendation to the user based on the classifying of the labeled first user behavior; providing, by the recommendation engine, the recommendation to the user; receiving, by the recommendation engine, a second user behavior data, the second user behavior data associated with a user response by the user to the recommendation; and evaluating, by the recommendation engine, the second user behavior data and performing a corrective action to the recommendation model.

Self-healing recommendation engine

Methods and systems for notifying users of new applications

A computer-implemented method and system may include identifying, by a recommendation engine, a first plurality of images in a first theme that includes a first layout characteristic, determining a first plurality of vector representations of the first plurality of images using an image processing model that includes a layered matrix of nodes with weighted interconnections, identifying at least a second plurality of images in a second theme that includes a second layout characteristic that is different from the first layout characteristic, determining a second plurality of vector representations of the second plurality of images, computing a vector difference between the first plurality of vector representations and the second plurality of vector representations; determining and communicating a recommendation based on the computed vector difference, and mapping received feedback to the weightings of the interconnections to improve a future recommendation.

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Montreal, Canada

Putra Manggala