Speeding Up Recommender Systems Using Association Rules

TL;DR

This paper introduces FMAR, a novel recommender system that combines Factorization Machines with association rules to significantly speed up recommendation generation without sacrificing accuracy.

Contribution

The paper proposes a new hybrid approach using association rules with Factorization Machines to reduce prediction time in recommender systems.

Findings

FMAR reduces recommendation prediction time significantly.

The accuracy of recommendations remains comparable to traditional methods.

Association rules effectively filter items, decreasing computational load.

Abstract

Recommender systems are considered one of the most rapidly growing branches of Artificial Intelligence. The demand for finding more efficient techniques to generate recommendations becomes urgent. However, many recommendations become useless if there is a delay in generating and showing them to the user. Therefore, we focus on improving the speed of recommendation systems without impacting the accuracy. In this paper, we suggest a novel recommender system based on Factorization Machines and Association Rules (FMAR). We introduce an approach to generate association rules using two algorithms: (i) apriori and (ii) frequent pattern (FP) growth. These association rules will be utilized to reduce the number of items passed to the factorization machines recommendation model. We show that FMAR has significantly decreased the number of new items that the recommender system has to predict and hence, decreased the required time for generating the recommendations. On the other hand, while building the FMAR tool, we concentrate on making a balance between prediction time and accuracy of generated recommendations to ensure that the accuracy is not significantly impacted compared to the accuracy of using factorization machines without association rules.