collaborative filtering

Applications that want to use the recommendation service have a unique account key and a secret key associated with them. This website allows you to create one or more key pairs. Register here to try Sugestio free for thirty days.
After logging in, go to My Projects and click on "more details" to get some basic account information. We also keep a log of your service calls. We log requests that return either 202, 400 or 404 HTTP response codes. This way, you can check if all the data made it through okay, and you can review information on failed requests.
For free Developer accounts, we offer both personal recommendations and similar item recommendations based on standard collaborative filtering techniques. This algorithm requires only user behaviour (rating data) as input, so it is not really necessary to submit metadata about items or users. (but you may certainly do so, if your application involves rich content!)
While your developer account is active, our server will periodically update your recommendations based on the latest consumption data. Collaborative Filtering looks for users that exhibit similar behaviour, and recommends items that the user has not yet discovered. Let's assume that you have submitted the following five star ratings:

  • User A, item 1: 4 stars
  • User A, item 2: 5 stars
  • User A, item 3: 5 stars
  • User B, item 2: 4 stars
  • User B, item 3: 4 stars
  • User B, item 4: 5 stars

Users A and B are quite similar, having both given positive ratings to items 2 and 3. User A also liked item 1, which user B has not yet discovered. The next time recommendations are generated, user B will get item 1 in his list of recommended content. User A will get item 4 in theirs.

In recent years, a variety of techniques have been proposed for calculating personal recommendations, including collaborative filtering, content-based methods and knowledge-based techniques. To improve performance, these algorithms can be combined in hybrid recommenders. The Sugestio team is also doing research on various hybrid recommendation techniques. Here, we provide a brief overview of the state of the art.

A merging solution is a simple technique that combines the recommendations of multiple recommenders. Top-N recommendation lists of the various algorithms are joined to create a new list consisting of a mix of these suggestions. A more intelligent way to combine the recommendations of different algorithms is by using a weighting scheme. These weighting solutions combine the scores of several recommender systems into a final recommendation score based on weights expressing the confidence in the various algorithms. These algorithm weights may be altered during operation to fine-tune the hybrid recommender.
Instead of combining the recommendations, switching solutions select the best recommendation algorithm depending on the available data. This hybrid recommender might select a content-based algorithm for sparse profile data and switch to a collaborative filter when more data becomes available. Cascade solutions consist of multiple algorithms refining each other. This technique could be applied to restaurant recommendations: a collaborative filtering algorithm determines the best restaurants according to the personal profile of the end-user. Next, a knowledge-based filter eliminates irrelevant restaurants based on the kitchen type, geographical location, price range, etc.

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