Latent factorization-based recommendation system

Latent factorization-based filter recommendation methods attempt to discover latent features to represent user and item profiles by decomposing the ratings. Unlike the content-based filtering features, these latent features are not interpretable and can represent complicated features. For instance, in a movie recommendation system, one of the latent features might represent a linear combination of humor, suspense, and romance in a specific proportion. Generally, for already rated items, the rating r_ij given by an user i to an item j can be represented as . where u_i is the user profile vector based on the latent factors and v_i is the item vector based on the same latent factors:

Figure 6.3: Latent factor-based filtering illustration

Illustrated in the previous diagram (Figure 6.3) is a latent-factor based recommendation method,...