Rescaling layer in neural network

A computer-implemented method for returning search results in an online computer system, the method comprising:

• retrieving a plurality of sample member profiles of members of an online service, a plurality of sample member labels, and activity and usage information pertaining to actions taken by those members on the online service;
• for each sample member profile in the plurality of sample member profiles:
• parsing the sample member profile and the activity and usage information pertaining to actions taken by a member corresponding to the sample member profile on the online service to extract a first set of one or more features;
• parsing an associated query to obtain one or more query features;
• feeding sample member labels, the extracted first set of one or more features and the extracted one or more query features into a first machine learning algorithm to train a neural network to output a score indicative of a probability that a searcher will select a potential search result corresponding to the sample member profile and a probability that a member corresponding to the sample member profile will respond to a communication from a searcher, the neural network containing a rescaling layer, the rescaling layer learning rescaling transformations for one or more features during the training of the neural network.

A non-transitory machine-readable storage medium comprising instructions, which when implemented by one or more machines, cause the one or more machines to perform operations comprising:

• retrieving a plurality of sample member profiles of members of an online service, a plurality of sample member labels, and activity and usage information pertaining to actions taken by those members on the online service;
• for each sample member profile in the plurality of sample member profiles:
• parsing the sample member profile and the activity and usage information pertaining to actions taken by a member corresponding to the sample member profile on the online service to extract a first set of one or more features;
• parsing an associated query to obtain one or more query features;
• feeding sample member labels, the extracted first set of one or more features and the extracted one or more query features into a first machine learning algorithm to train a neural network to output a score indicative of a probability that a searcher will select a potential search result corresponding to the sample member profile and a probability that a member corresponding to the sample member profile will respond to a communication from a searcher, the neural network containing a rescaling layer, the rescaling layer learning rescaling transformations for one or more features during the training of the neural network.