Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model

“In order to establish recommendations, CF systems need to compare fundamentally different objects: items against users. There are two primary approaches to facilitate such a comparison, which constitute the two main disciplines of CF: the neighborhood approach and latent factor models.

Neighborhood methods are centered on computing the relationships between items or, alternatively, between users. An item-oriented approach evaluates the preference of a user to an item based on ratings of similar items by the same user. In a sense, these methods transform users to the item space by viewing them as baskets of rated items. This way, we no longer need to compare users to items, but rather directly relate items to items.

Latent factor models, such as Singular Value Decomposition (SVD), comprise an alternative approach by transforming both items and users to the same latent factor space, thus making them directly comparable. The latent space tries to explain ratings by characterizing both products and users on factors automatically inferred from user feedback. For example, when the products are movies, factors might measure obvious dimensions such as comedy vs. drama, amount of action, or orientation to children; less well defined dimensions such as depth of character development or “quirkiness”; or completely uninterpretable dimensions.”