Cold-start recommendation through granular association rules

TL;DR

This paper introduces a novel cold-start recommendation method using granular association rules to effectively recommend items to new users and new items, demonstrated on the MovieLens dataset.

Contribution

It proposes a new approach for cold-start recommendation that models users and items with information granules and generates association rules for recommendations.

Findings

Rule sets perform consistently on training and testing data.

Proper granule setting is crucial for effective recommendations.

The approach is validated on the MovieLens dataset.

Abstract

Recommender systems are popular in e-commerce as they suggest items of interest to users. Researchers have addressed the cold-start problem where either the user or the item is new. However, the situation with both new user and new item has seldom been considered. In this paper, we propose a cold-start recommendation approach to this situation based on granular association rules. Specifically, we provide a means for describing users and items through information granules, a means for generating association rules between users and items, and a means for recommending items to users using these rules. Experiments are undertaken on a publicly available dataset MovieLens. Results indicate that rule sets perform similarly on the training and the testing sets, and the appropriate setting of granule is essential to the application of granular association rules.