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Creating a custom recommender using taste

Taste is a great framework for collaborative filtering.  We are going to be launching a new recommendation algorithm on our site (MyFriendSuggests.com) in the coming weeks (Stay Tuned!) based on the Taste framework.  Taste provides a User-based and Item-based recommender.  User based recommenders find users that have similiar tastes to you and then use their ratings to predict how you might rate a given item.  Item based recommenders find items that are similar to each others and use those similar items to predict how you might rate a given item.  In our testing we found that a recommender that uses both types of recommenders would be most effective.  Basically we use the following formulat to predict user u’s rating of object x.

P(u,x) = alpha*uRec(u,x) + (1-alpha) * iRec(u,x)

Where alpha is a constant between 0 and 1 (basically weighting the two recommenders) and uRec and iRec are the Taste User and Item based recommenders.

Using the Taste evaluators you can build a simple program to find the bast value of alpha for your application.  Since we still have very sparse data we are leaving the value 0.50 until we have more data to work with.  In the next few days I’ll be posting some more on how I used taste to build our recommender.

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