Title
NCDREC: A Decomposability Inspired Framework for Top-N Recommendation
Abstract
Building on the intuition behind Nearly Decomposable systems, we propose NCDREC, a top-N recommendation framework designed to exploit the innately hierarchical structure of the item space to alleviate Sparsity, and the limitations it imposes to the quality of recommendations. We decompose the item space to define blocks of closely related elements and we introduce corresponding indirect proximity components that try to fill in the gap left by the inherent sparsity of the data. 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 Movie Lens 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.
Year
DOI
Venue
2014
10.1109/WI-IAT.2014.32
IAT), 2014 IEEE/WIC/ACM International Joint Conferences  
Keywords
Field
DocType
information filtering,recommender systems,MovieLens dataset,NCDREC,Yahoo!R2Music dataset,cold-start recommendation scenarios,decomposability inspired framework,full item space coverage,indirect proximity components,information filtering,item space decomposition,nearly decomposable systems,performance metrics,recommendation accuracy,recommendation diversity,sparseness insensitivity,sufficient conditions,top-N recommendation framework,Decomposability,Long-Tail Recommendation,Markov Chain Models,Ranking,Recommender Systems,Sparsity
Recommender system,Data mining,Markov process,Ranking,Computer science,Matrix decomposition,Intuition,Exploit,Sparse matrix
Conference
Volume
ISBN
Citations 
1
978-1-4799-4143-8-01
8
PageRank 
References 
Authors
0.49
18
2
Name
Order
Citations
PageRank
Athanasios N. Nikolakopoulos1599.02
John D. Garofalakis217636.73