Top-N recommendations in the presence of sparsity: An NCD-based approach
This addresses the sparsity problem in collaborative filtering for recommendation systems, offering a novel theoretical approach with practical gains.
The paper tackles the challenge of making recommendations under data sparsity by modeling the item space as a Nearly Decomposable system, defining blocks of related items to improve coverage. Experiments on MovieLens and Yahoo!R2Music datasets show that NCDREC outperforms state-of-the-art algorithms in accuracy, diversity, and sparseness insensitivity.
Making recommendations in the presence of sparsity is known to present one of the most challenging problems faced by collaborative filtering methods. In this work we tackle this problem by exploiting the innately hierarchical structure of the item space following an approach inspired by the theory of Decomposability. We view the itemspace as a Nearly Decomposable system and we define blocks of closely related elements and corresponding indirect proximity components. We study the theoretical properties of the decomposition and we derive sufficient conditions that guarantee full item space coverage even in cold-start recommendation scenarios. A comprehensive set of experiments on the MovieLens and the Yahoo!R2Music datasets, using several widely applied performance metrics, support our model's theoretically predicted properties and verify that NCDREC outperforms several state-of-the-art algorithms, in terms of recommendation accuracy, diversity and sparseness insensitivity.