Towards Improving Top-N Recommendation by Generalization of SLIM.
Santiago Larrain, Denis Parra, Álvaro Soto · 2015
Sparse Linear Methods (SLIM) are state-of-the-art recommenda-tion approaches based on matrix factorization, which rely on a reg-ularized!1-norm and!2-norm optimization –an alternative opti-mization problem to the traditional Frobenious norm. Although they have shown outstanding performance in Top-N recommenda-tion, existent works have not yet analyzed some inherent assump-tions that can have an important effect on the performance of these algorithms. In this paper, we attempt to improve the performance of SLIM by proposing a generalized formulation of the aforemen-tioned assumptions. Instead of directly learning a sparse represen-tation of the user-item matrix, we (i) learn the latent factors ’ matrix of the users and the items via a traditional matrix factorization ap-proach, and then (ii) reconstruct the latent user or item matrix via prototypes which are learned using sparse coding, an alternative SLIM commonly used in the image processing domain. The re-sults show that by tuning the parameters of our generalized model we are able to outperform SLIM in several Top-N recommendation experiments conducted on two different datasets, using both nDCG and nDCG@10 as evaluation metrics. These preliminary results, although not conclusive, indicate a promising line of research to improve the performance of SLIM recommendation.