Abstract:
Recent work has shown that, despite their simplicity, item-based models optimised through ridge regression can attain highly competitive results on collaborative filtering tasks. As these models are analytically computable and thus forgo the need for often expensive iterative optimisation procedures, they are an attractive choice for practitioners. We study the applicability of such closed-form models to implicit-feedback collaborative filtering when additional side-information or metadata about items is available. Two complementary extensions to the easer paradigm are proposed, based on collective and additive models. Through an extensive empirical analysis on several large-scale datasets, we show that our methods can effectively exploit side-information whilst retaining a closed-form solution, and improve upon the state-of-the-art without increasing the computational complexity of the original easer approach. Additionally, empirical results demonstrate that the use of side-information leads to more “long tail” items being recommended, benefiting the recommendations’ coverage of the item catalogue.
