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I'm looking for a library I can use to match my users to other Django models based on answers to questions-- also my own django model.
So I'd like something customizable, with good documentation/support, and hopefully not too hard to implement!
Does anyone have any good recommendations? I've looked over Crab and Django-recommender, but neither seem to be very well documented.
basically what I have is two survey applications, with corresponding, but not identical, questions and answers. E.g. a question in app1 could be "how many nights a week do you drink?" and a question in app2 could be "how many nights a week do you expect to drink?", with a foreign key to the first question in the instance. I want to take the responses to these questions and use them to pair users from each set with each other, to give the users in group 2 recommendations based on what the users in group 1 already use.
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Steps Involved in Collaborative Filtering To build a system that can automatically recommend items to users based on the preferences of other users, the first step is to find similar users or items. The second step is to predict the ratings of the items that are not yet rated by a user.
In a content-based recommendation system, first, we need to create a profile for each item, which represents the properties of those items. From the user profiles are inferred for a particular user. We use these user profiles to recommend the items to the users from the catalog.
The two most commonly used recommender systems are content-based filtering and collaborative filtering. In this post, we will focus on collaborative filtering as this is used by Netflix to make our Sundays more enjoyable. Collaborative filtering systems suggest items based on users' preferences historically.
Which technique is proper for solving collaborative filtering problem? The standard method of Collaborative Filtering is known as Nearest Neighborhood algorithm. There are user-based CF and item-based CF.
They covered this subject in the free Stanford ML class. Check the videos for chapter XVI at http://www.ml-class.org/course/video/preview_list
Although the implementation discussed is Matlab/Octave it should be not difficult to implement in Python, even easier if you are using Numpy
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