RAPS: A Recommender Algorithm Based on Pattern Structures
This work addresses recommendation accuracy for users in systems with numeric ratings, but it appears incremental as it builds on existing methods like Slope One.
The authors tackled the problem of generating personalized recommendations in numeric rating systems by proposing RAPS, a new algorithm based on Pattern Structures, which achieved the best or comparable precision and recall compared to the state-of-the-art Slope One algorithm on the Movie Lens dataset.
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.