Search result ranking using machine learning

1. A method comprising:

implementing a goal model for a first goal from a plurality of goals using machine learning, the goal model being at least one of a neural network or an expert system;

factorizing raw data to a set of data factors for the first goal, the raw data including query data from a user search query and at least one of relevance of an item title, temporal data, transaction data, impressions of item listings, item demand, or item supply;

assigning a plurality of impact scores to each of the set of data factors, a first impact score of the plurality of impact scores corresponding to the first goal and a second impact score of the plurality of impact scores corresponding to a second goal in the plurality of goals, the plurality of impact scores respectively measuring the degree to which changes in a data factor influence a corresponding model's output, the first impact score corresponding to revenue generation and the second impact score corresponding to cross product marketing;

ranking the set of data factors based on the first impact score;

selecting a plurality of data factors from the set of data factors based on the ranking, the plurality of data factors being a proper subset of the set of data factors;

modifying the values of the plurality of data factors by respective weights prior to being inputted into the goal model;

inputting, responsive to the search query, the plurality of data factors into the goal model to:

select search results from a database using the goal model; and

create a model output, the model output including a ranking for each result in the search results; and

presenting, to a user, an ordered list of search results based on the ranking for each result in the search results from the model output.

2. The method of claim 1 , wherein implementing a goal model using machine learning includes training a machine learning system towards a goal, wherein the machine learning system is the at least one of the neural network or the expert system.

3. The method of claim 2 , wherein training the machine learning system towards a goal includes:

identifying the goal;

identifying an input dataset relevant to the goal;

inputting the input dataset to the machine learning system;

observing an output of the machine learning system to identify errors in the output based on the goal; and

responsive to receiving the errors in the output, iteratively refining the output until a predetermined condition is met.

4. The method of claim 3 , wherein iteratively refining the output includes:

adjusting an internal model of the machine learning system based on the errors in the output;

re-inputting the input dataset to the machine learning system; and

observing intermediate refined output of the machine learning system to identify further errors in the intermediate refined output.

5. The method of claim 3 , wherein an input dataset comprises at least one data factor.

6. The method of claim 5 , wherein the data factor includes user data from an online publisher.

7. The method of claim 5 , wherein the data factor includes transaction data from an online publisher.

8. The method of claim 5 , wherein the data factor includes publication data from an online publisher.

9. The method of claim 5 , wherein the data factor comprises aggregated data from an online publisher.

10. The method of claim 5 , wherein the data factor comprises data from the search query.

11. The method of claim 1 , wherein at least one data factor from the plurality of data factors is computed prior to the search query.

12. The method of claim 1 , wherein at least one data factor from the plurality of data factors is computed following the search query.

13. A system comprising:

execution hardware;

a machine learning module, performed by the execution hardware, configured to implement a goal model for a first goal from a plurality of goals, the goal model being at least one of a neural network or an expert system;

a factorization module, performed by the execution hardware, configured to:

factorize raw data to a set of data factors for the first goal, the raw data including query data from a user search query and at least one of relevance of an item title, temporal data, transaction data, impressions of item listings, item demand, or item supply;

assign a plurality of impact scores to each of the set of data factors, a first impact score of the plurality of impact scores corresponding to the first goal and a second impact score of the plurality of impact scores corresponding to a second goal in the plurality of goals, the plurality of impact scores respectively measuring the degree to which changes in a data factor influence a corresponding model's output, the first impact score corresponding to revenue generation and the second impact score corresponding to cross product marketing;

rank the set of data factors based on the first impact score;

select a plurality of data factors from the set of data factors based on the ranking; and

modify the values of the plurality of data factors by respective weights prior to being inputted into the goal model; and

a goal module, performed by the execution hardware, configured to:

input, responsive to the search query, the plurality of data factors into the goal model to:

select search results from a database using the goal model; and

create a model output, the model output including a ranking for each result in the search results; and

present, to a user, an ordered list of search results based on the ranking for each result in the search results from the model output.

14. The system of claim 13 , comprising a training module configured to train a machine learning system towards a goal, wherein the machine learning system is the at least one of the neural network or the expert system.

15. The system of claim 14 , wherein the training module if configured to:

receive identification of the goal;

receive identification of an input dataset relevant to the goal;

input the input dataset to the machine learning system;

observe an output of the machine learning system and identify errors in the output based on the goal; and

responsive to receiving the errors in the output, iteratively refine the output until a predetermined condition is met.

16. The system of claim 15 , wherein to iteratively refine the output the training module is configured to:

adjust an internal model of the machine learning system based on the errors in the output;

re-input the input dataset to the machine learning system; and

observe an intermediate refined output of the machine learning system and identify further errors in the intermediate refined output.

17. The system of claim 15 , wherein an input dataset comprises at least one data factor.

18. The system of claim 17 , wherein the data factor includes user data from an online publisher.

19. The system of claim 17 , wherein the data factor includes transaction data from an online publisher.

20. The system of claim 17 , wherein the data factor includes publication data from an online publisher.

21. The system of claim 17 , wherein the data factor comprises aggregated data from an online publisher.

22. The system of claim 17 , wherein the data factor comprises data from the search query.

23. The system of claim 13 , wherein at least one data factor from the plurality of data factors is computed prior to the search query.

24. The system of claim 13 , wherein at least one data factor from the plurality of data factors is computed following the search query.

25. A non-transitory machine readable medium including instructions that, when executed, cause a machine to perform operations including:

implementing a goal model for a first goal from a plurality of goals using machine learning, the goal model being at least one of a neural network or an expert system;

factorizing raw data to a set of data factors for the first goal, the raw data including query data from a user search query and at least one of relevance of an item title, temporal data, transaction data, impressions of item listings, item demand, or item supply;

assigning a plurality of impact scores to each of the set of data factors, a first impact score of the plurality of impact scores corresponding to the first goal and a second impact score of the plurality of impact scores corresponding to a second goal in the plurality of goals, the plurality of impact scores respectively measuring the degree to which changes in a data factor influence a corresponding model's output, the first impact score corresponding to revenue generation and the second impact score corresponding to cross product marketing;

ranking the set of data factors based on the first impact score;

selecting a plurality of data factors from the set of data factors based on the ranking, the plurality of data factors being a proper subset of the set of data factors;

modifying the values of the plurality of data factors by respective weights prior to being inputted into the goal model;

inputting, responsive to the search query, the plurality of data factors into the goal model to:

select search results from a database using the goal model; and

create a model output, the model output including a ranking for each result in the search results; and

presenting, to a user, an ordered list of search results based on the ranking for each result in the search results from the model output.