Traditional Collaborative Filtering
A traditional collaborative filtering algorithm represents a customer as an N-dimensional vector of items, where N is the number of distinct catalog items. The components of the vector are positive for purchased or positively rated items and negative for negatively rated items. To compensate for best-selling items, the algorithm typically multiplies the vector components by the inverse frequency (the inverse of the number of customers who have purchased or rated the item), making less well-known items much more relevant.3 For almost all customers, this vector is extremely sparse. The algorithm generates recommendations based on a few customers who are most similar to the user. It can measure the similarity of two customers, A and B, in various ways; a common method is to measure the cosine of the angle between the two vectors:
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Overview of Collaberative filters, cluster models, and other recommendation algorithms. |
