statduck

The basic assumption is that significant portion of the rows and columns of data matrix are highly correlated. Highly correlated data can be well explained by a low number of columns, so low-rank matrix is useful for the matrix estimation. It reduces the dimensionality by rotating the axis system, so that pairwise correlations between dimensions are removed. Low Rank Approach ✏️ $R$ has a rank $k

Reference: Charu C. Aggarwal. Recommender Systems: The Textbook. Springer Neighborhood-Based method 1. Introduction Motivation This algorithm assumes that similar users show similar patterns in rating. It can be categorized into to methods. User-based collaborative filtering(cf): The predicted ratings of user A are computed from the peer group ratings. Item-based collaborative filtering(cf): The..