Recommendations now update in near real-time

Submitted by Sugestio on 3 May 2011 - 3:50pm

Established algorithms like Collaborative Filtering recommend content based on the behavior data of all your users. This way, updating the recommendations for a single user requires too much time and processing power to keep up with their current actions. The easy solution is to update recommendations periodically for all users, but this leaves them lagging way behind. Now, the latest version of our API can recommend content to your users based on their most recent activity. To use this new feature, add the parameter *engine=instant* to your API calls:

GET /sites/{account}/users/{userid}/recommendations.xml?engine=instant

While this technique may be somewhat less accurate, recommendations do update in near real time. Combining these instant recommendations with periodically calculated ones gives you the best of both worlds.

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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