EigenRec: generalizing PureSVD for effective and efficient top-N recommendations

Autor: Athanasios N. Nikolakopoulos, John Garofalakis, Vassilis Kalantzis, Efstratios Gallopoulos
Rok vydání: 2018
Předmět:
FOS: Computer and information sciences
Similarity (geometry)
Computer science
G.1.3
02 engineering and technology
Machine learning
computer.software_genre
MovieLens
Computer Science - Information Retrieval
Set (abstract data type)
Operator (computer programming)
Computer Science - Databases
H.3.3
Artificial Intelligence
020204 information systems
Component (UML)
H.2.8
FOS: Mathematics
0202 electrical engineering
electronic engineering
information engineering
Collaborative filtering
Special case
Social and Information Networks (cs.SI)
business.industry
Computer Science - Numerical Analysis
Computer Science - Social and Information Networks
Databases (cs.DB)
Numerical Analysis (math.NA)
Human-Computer Interaction
Computer Science - Distributed
Parallel
and Cluster Computing
Hardware and Architecture
Path (graph theory)
Distributed
Parallel
and Cluster Computing (cs.DC)
Artificial intelligence
business
computer
Information Retrieval (cs.IR)
Software
Information Systems
Zdroj: Knowledge and Information Systems. 58:59-81
ISSN: 0219-3116
0219-1377
Popis: We introduce EigenRec; a versatile and efficient Latent-Factor framework for Top-N Recommendations that includes the well-known PureSVD algorithm as a special case. EigenRec builds a low dimensional model of an inter-item proximity matrix that combines a similarity component, with a scaling operator, designed to control the influence of the prior item popularity on the final model. Seeing PureSVD within our framework provides intuition about its inner workings, exposes its inherent limitations, and also, paves the path towards painlessly improving its recommendation performance. A comprehensive set of experiments on the MovieLens and the Yahoo datasets based on widely applied performance metrics, indicate that EigenRec outperforms several state-of-the-art algorithms, in terms of Standard and Long-Tail recommendation accuracy, exhibiting low susceptibility to sparsity, even in its most extreme manifestations -- the Cold-Start problems. At the same time EigenRec has an attractive computational profile and it can apply readily in large-scale recommendation settings.
Comment: 23 pages. Journal version of the conference paper "Factored Proximity Models for Top-N Recommendation"
Databáze: OpenAIRE
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