Generating training data based on gaze captured at a source location for training a replacement model

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:

• receiving, at an online system, information captured by a gaze tracking device describing a gaze point of a user and video data captured within a source location;
• detecting, within the source location, an item location associated with a first item that matches the gaze point of the user based at least in part on the received information;
• determining that the first item is not available at the source location based at least in part on the video data;
• receiving a signal indicating that the user collected a second item from the source location;
• determining that the second item is a replacement for the first item;
• generating a new training example for a training dataset, wherein the new training example indicates the second item is an acceptable replacement for the first item for the user;
• and
• training a machine-learning model to generate a score indicating whether a candidate item is an acceptable replacement for a target item for a particular user of the online system, wherein the machine-learning model is trained using the training dataset that includes the new training example.

A computer system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, perform actions comprising:
• receiving, at an online system, information captured by a gaze tracking device describing a gaze point of a user and video data captured within a source location;
• detecting, within the source location, an item location associated with a first item that matches the gaze point of the user based at least in part on the received information;
• determining that the first item is not available at the source location based at least in part on the video data;
• receiving a signal indicating that the user collected a second item from the source location;
• determining that the second item is a replacement for the first item;
• generating a new training example for a training dataset, wherein the new training example indicates the second item is an acceptable replacement for the first item for the user;
• training a machine-learning model to generate a score indicating whether a candidate item is an acceptable replacement for a target item for a particular user of the online system, wherein the machine-learning model is trained using the training dataset that includes the new training example;
• and
• storing parameters of the trained machine-learning model on a non-transitory computer-readable medium.