Alternating least squares (ALS) is a powerful matrix factorization (MF) algorithm for both implicit and explicit feedback based recommender systems. We show that by using the Sherman-Morrison formula (SMF), we can reduce the computational complexity of several ALS based algorithms. It also reduces the complexity of greedy forward and backward feature selection algorithms by an order of magnitude. We propose linear kernel ridge regression (KRR) for users with few ratings. We show that both SMF and KRR can efficiently handle new ratings.
Swipe to navigate through the chapters of this book
Please log in to get access to this content
To get access to this content you need the following product:
- Computational Complexity Reduction for Factorization-Based Collaborative Filtering Algorithms
- Springer Berlin Heidelberg
- Sequence number
Neuer Inhalt/© ITandMEDIA