Per-Instance Algorithm Selection for Recommender Systems via Instance Clustering
This work addresses the challenge of per-instance algorithm selection for recommender systems, a problem previously unsolved, offering a method to improve recommendation accuracy for users.
This paper proposes a per-instance meta-learner for recommender systems that clusters data instances and predicts the best algorithm for unseen instances based on cluster membership. The approach, tested with 14 filtering algorithms, achieved a significant improvement over the best-performing base algorithm, reducing MAE from 0.7214 to 0.7107.
Recommendation algorithms perform differently if the users, recommendation contexts, applications, and user interfaces vary even slightly. It is similarly observed in other fields, such as combinatorial problem solving, that algorithms perform differently for each instance presented. In those fields, meta-learning is successfully used to predict an optimal algorithm for each instance, to improve overall system performance. Per-instance algorithm selection has thus far been unsuccessful for recommender systems. In this paper we propose a per-instance meta-learner that clusters data instances and predicts the best algorithm for unseen instances according to cluster membership. We test our approach using 10 collaborative- and 4 content-based filtering algorithms, for varying clustering parameters, and find a significant improvement over the best performing base algorithm at alpha=0.053 (MAE: 0.7107 vs LightGBM 0.7214; t-test). We also explore the performances of our base algorithms on a ratings dataset and empirically show that the error of a perfect algorithm selector monotonically decreases for larger pools of algorithm. To the best of our knowledge, this is the first effective meta-learning technique for per-instance algorithm selection in recommender systems.