In many machine learning problems, we seek to learn how much various people prefer a varied set of objects. Using the accelerated Trace norm prediction algorithm from (Wang KDD '14), we can both generate directed queries and form predictions based on those queries. As a result, the number of questions required is dramatically reduced.

Suppose we have three people and five movies, and we wish to recover which movies are preferred by each person. Suppose also that we can only ask five questions of these people, but that we decide which person to ask next.

To do that, run the following:

eng := collaborativepermute.NewEngine(3, 5)

for i := 0; i < 5; i++ {
	q := eng.Generate(-1)
	// display q to user, update order of q.Choices
	q.Respond(q)
}

Currently, the implementation will only ever ask about two items at a time. If you cannot decide when each user is prompted (such as for an online form), pass the current user's ID to .Generate to restrict the queries generated.

The code in this repository is covered under the MIT License:

Copyright (c) 2015 Jeremy Archer

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

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