Computational complexity reduction for factorization-based collaborative filtering algorithms

István Pilászy, Domonkos Tikk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Alternating least squares (ALS) is a powerful matrix factorization (MF) algorithm for both implicit and explicit feedback based recommender systems. We show that by using the Sherman-Morrison formula (SMF), we can reduce the computational complexity of several ALS based algorithms. It also reduces the complexity of greedy forward and backward feature selection algorithms by an order of magnitude. We propose linear kernel ridge regression (KRR) for users with few ratings. We show that both SMF and KRR can efficiently handle new ratings.

Original languageEnglish
Title of host publicationE-Commerce and Web Technologies - 10th International Conference, EC-Web 2009, Proceedings
Pages229-239
Number of pages11
DOIs
Publication statusPublished - Nov 4 2009
Event10th International Conference on E-Commerce and Web Technologies, EC-Web 2009 - Linz, Austria
Duration: Sep 1 2009Sep 4 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5692 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on E-Commerce and Web Technologies, EC-Web 2009
CountryAustria
CityLinz
Period9/1/099/4/09

Keywords

  • Alternating least squares
  • Collaborative filtering
  • Greedy feature selection
  • Kernel ridge regression
  • Matrix factorization
  • Sherman-Morrison formula

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Computational complexity reduction for factorization-based collaborative filtering algorithms'. Together they form a unique fingerprint.

  • Cite this

    Pilászy, I., & Tikk, D. (2009). Computational complexity reduction for factorization-based collaborative filtering algorithms. In E-Commerce and Web Technologies - 10th International Conference, EC-Web 2009, Proceedings (pp. 229-239). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5692 LNCS). https://doi.org/10.1007/978-3-642-03964-5-22