Concetti Chiave
Introducing data-driven metrics to quantify the predictability of recommender systems based on the structural complexity of the user-item rating matrix.
Sintesi
The paper proposes two strategies to measure the predictability of collaborative filtering recommender systems (RS) datasets:
Analytical Structural Consistency (ASC):
Adapts the notion of structural consistency in graphs to work for non-square, weighted RS matrices using singular value decomposition (SVD).
Perturbs the rating matrix, computes the SVD of the perturbed matrix, and approximates the structural differences between the original and perturbed matrices.
Uses the root mean square error (RMSE) of the perturbed matrix prediction as the predictability metric.
Empirical Structural Consistency (ESC):
A simpler version of ASC that does not require a closed-form approximation.
Randomly permutes a fraction of the known ratings, trains a truncated SVD (TSVD) recommendation algorithm on the perturbed matrix, and predicts the held-out ratings.
Uses the average RMSE of the predicted ratings as the predictability metric.
The authors conduct experiments on a sample of 12 real-world datasets and manually generated datasets to evaluate the relationship between the proposed predictability metrics and the accuracy of well-known collaborative filtering algorithms. The results show a strong correlation between the metrics and the best-performing algorithm's prediction accuracy, indicating the metrics' ability to capture the inherent predictability of the data.
The authors argue that the proposed metrics can be integrated into existing recommender systems to improve their performance by evaluating algorithm performance, capturing changes in the system's evolution, and providing insights into the effects of feedback loops.
Statistiche
The dataset details and RMSE values for the tested collaborative filtering algorithms are provided in Table 1.
Citazioni
No significant quotes were found.