Recommendations using rule combinations

What is it about?

We propose a method to improve the quality of recommendations by combining association rules in a principled manner. It differs from existing rule-combination methods in that it is strongly grounded in probability theory. Combining rules requires the identification of the best combination of rules from the many combinations that might exist. We use a maximum-likelihood framework to compare alternative combinations. We show that this problem can equivalently be represented as a set partitioning problem by translating it into an information theoretic context – the best solution corresponds to the set of rules that leads to the highest sum of mutual information associated with the rules. Experiments to evaluate the quality of recommendations made using the proposed approach show that (i) a greedy heuristic used to solve the set partitioning problem is very effective, providing results comparable to those from using the optimal set partitioning solution, (ii) the recommendations made by our approach are more accurate than those made by a variety of state-of-the-art benchmarks, including collaborative filtering and matrix factorization, and (iii) the recommendations can be made in a fraction of a second on a desktop computer, making it practical to use in real-world applications.

Featured Image

Photo by petr sidorov on Unsplash

Photo by petr sidorov on Unsplash

Why is it important?

The performance relative to state-of-the-art approaches such as matrix factorization and collaborative filtering indicates that it is a viable alternative in practice.

Perspectives