Embedding layer in neural network for ranking candidates

The system of claim, wherein the instructions further cause the system to:

• obtain a plurality of candidate search results in response to a query corresponding to a searcher;
• obtain a member profile, in an online service, corresponding to the searcher;
• for each candidate search result from the plurality of candidate search results:
• obtain activity and usage information for a member corresponding to the candidate search result;
• parse the candidate search result, the activity and usage information for the member corresponding to the candidate search result, and the member profile corresponding to the searcher to extract a second set of one or more features and a third set of one or more features;
• parse the query to obtain one or more query features;
• feed the extracted and one or more query features into the neural network, outputting a score for the candidate search result;
• rank the plurality of candidate search results by their corresponding scores;
• and
• return one or more of the plurality of candidate search results based on the ranking.

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 first 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 an embedding layer that, during training of the neural network, adjusts one or more embeddings of values of features, each embedding indicating a representation of a value of a feature in a dimensional space.

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 first 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 an embedding layer that, during training of the neural network, adjusts one or more embeddings of values of features, each embedding indicating a representation of a value of a feature in a dimensional space.