SUGGEST A Top-N recommendation engine that implements a variety of recommendation algorithm
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SUGGEST Tags

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SUGGEST Description

SUGGEST was developed to be a Top-N recommendation engine that implements a variety of recommendation algorithms. Top-N recommender systems, a personalized information filtering technology, are used to identify a set of N items that will be of interest to a certain user. In recent years, top-N recommender systems have been used in a number of different applications such to recommend products a customer will most likely buy; recommend movies, TV programs, or music a user will find enjoyable; identify web-pages that will be of interest; or even suggest alternate ways of searching for information. The algorithms implemented by SUGGEST are based on collaborative filtering that is the most successful and widely used framework for building recommender systems. SUGGEST implements two classes of collaborative filtering-based top-N recommendation algorithms, called user-based and item-based.

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