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The goal of a collaborative filtering algorithm is to suggest new items or to predict the utility of a certain item for a particular user based on the user's previous likings and the opinions of other like-minded users. In a typical CF scenario, there is a list of m users and a list of n items . Each user ui has a list of items Iui, which the user has expressed his/her opinions about. Opinions can be explicitly given by the user as a rating score, generally within a certain numerical scale, or can be implicitly derived from purchase records, by analyzing timing logs, by mining web hyperlinks and so on [28,16]. Note that and it is possible for Iui to be a null-set. There exists a distinguished user called the active user for whom the task of a collaborative filtering algorithm is to find an item likeliness that can be of two forms.
- Prediction is a numerical value, Pa,j, expressing the predicted likeliness of item for the active user ua. This predicted value is within the same scale (e.g., from 1 to 5) as the opinion values provided by ua.
- Recommendation is a list of N items,
that the active user will like the most. Note that
the recommended list must be on items not already purchased by the
active user, i.e.,
This interface of
CF algorithms is also known as Top-N recommendation.
Figure 1 shows the schematic diagram of the collaborative filtering process. CF algorithms represent the entire user-item data as a ratings matrix, . Each entry ai,j in represent the preference score (ratings) of the ith user on the jth item. Each individual ratings is within a numerical scale and it can as well be 0 indicating that the user has not yet rated that item. Researchers have devised a number of collaborative filtering algorithms that can be divided into two main categories-Memory-based (user-based) and Model-based (item-based) algorithms . In this section we provide a detailed analysis of CF-based recommender system algorithms.
Next: Memory-based Collaborative Filtering Algorithms. Up: Collaborative Filtering Based Recommender Previous: Collaborative Filtering Based Recommender Badrul M. Sarwar