Mining top-k granular association rules for recommendation

TL;DR

This paper introduces a method to mine the top-k granular association rules for individual users in recommender systems, improving recommendation coverage and accuracy by ranking rules based on confidence.

Contribution

It proposes a novel approach to mine user-specific top-k granular association rules using confidence, source, and target coverage measures, enhancing recommendation quality.

Findings

Rules ranked by confidence improve recommendation relevance.

Granule size tuning prevents over-fitting.

Experimental results on MovieLens show high accuracy.

Abstract

Recommender systems are important for e-commerce companies as well as researchers. Recently, granular association rules have been proposed for cold-start recommendation. However, existing approaches reserve only globally strong rules; therefore some users may receive no recommendation at all. In this paper, we propose to mine the top-k granular association rules for each user. First we define three measures of granular association rules. These are the source coverage which measures the user granule size, the target coverage which measures the item granule size, and the confidence which measures the strength of the association. With the confidence measure, rules can be ranked according to their strength. Then we propose algorithms for training the recommender and suggesting items to each user. Experimental are undertaken on a publicly available data set MovieLens. Results indicate that the appropriate setting of granule can avoid over-fitting and at the same time, help obtaining high recommending accuracy.