RAPS: A Recommender Algorithm Based on Pattern Structures

TL;DR

The paper introduces RAPS, a new recommender algorithm based on Pattern Structures for numeric ratings, which outperforms or matches existing methods like Slope One in accuracy on Movie Lens data.

Contribution

It presents a novel pattern-structure-based algorithm for recommender systems that improves prediction quality over state-of-the-art methods.

Findings

RAPS achieves higher precision and recall than Slope One.

The algorithm performs comparably or better on Movie Lens dataset.

It effectively utilizes rating patterns for recommendations.

Abstract

We propose a new algorithm for recommender systems with numeric ratings which is based on Pattern Structures (RAPS). As the input the algorithm takes rating matrix, e.g., such that it contains movies rated by users. For a target user, the algorithm returns a rated list of items (movies) based on its previous ratings and ratings of other users. We compare the results of the proposed algorithm in terms of precision and recall measures with Slope One, one of the state-of-the-art item-based algorithms, on Movie Lens dataset and RAPS demonstrates the best or comparable quality.