SLIM: Sparse Linear Methods for Top-N Recommender Systems

Um ótimo artigo de base teórica, relativo a geração de Top-N recomendações em cenários bem esparsos (e.g. sistema de rating 0-5 em que poucas pessoas fazem a anotação do rating, etc). Recentemente, esse problema de recomendar dentro de uma matriz muito esparsa foi o motivo pelo qual o Netflix mudou o seu sistema de Rating que era de 1 a 5 para jóia ou ruim. https://www.youtube.com/watch?time_continue=66&v=as4pZhodG5I   Em todo o caso vale a pena a leitura para ver a forma na qual os autores estão trabalhando nesse tipo de desafio.

Abstract: This paper focuses on developing effective and efficient algorithms for top-N recommender systems. A novel Sparse Linear Method (SLIM) is proposed, which generates top-N recommendations by aggregating from user purchase/rating profiles. A sparse aggregation coefficient matrix W is learned from SLIM by solving an `1-norm and `2-norm regularized optimization problem. W is demonstrated to produce high quality recommendations and its sparsity allows SLIM to generate recommendations very fast. A comprehensive set of experiments is conducted by comparing the SLIM method and other state-of-the-art top-N recommendation methods. The experiments show that SLIM achieves significant improvements both in run time performance and recommendation quality over the best existing methods.