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Applications of the conjugate gradient method for implicit feedback collaborative filtering

Authors:

Gábor Takács,

István Pilászy,

Domonkos TikkAuthors Info & Claims

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

Pages 297 - 300

https://doi.org/10.1145/2043932.2043987

Published: 23 October 2011 Publication History

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Abstract

The need for solving weighted ridge regression (WRR) problems arises in a number of collaborative filtering (CF) algorithms. Often, there is not enough time to calculate the exact solution of the WRR problem, or it is not required. The conjugate gradient (CG) method is a state-of-the-art approach for the approximate solution of WRR problems. In this paper, we investigate some applications of the CG method for new and existing implicit feedback CF models. We demonstrate through experiments on the Netflix dataset that CG can be an efficient tool for training implicit feedback CF models.

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Index Terms

Applications of the conjugate gradient method for implicit feedback collaborative filtering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Markov decision processes
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Markov decision processes

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Published In

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

October 2011

414 pages

ISBN:9781450306836

DOI:10.1145/2043932

General Chairs:
Bamshad Mobasher
DePaul University, USA
,
Robin Burke
DePaul University, USA
,
Program Chairs:
Dietmar Jannach
TU Dortmund, Germany
,
Gediminas Adomavicius
University of Minnesota, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 October 2011

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RecSys '11: Fifth ACM Conference on Recommender Systems

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Recommendations

A Similarity Measure for Collaborative Filtering with Implicit Feedback

Using Implicit Feedback for Neighbors Selection: Alleviating the Sparsity Problem in Collaborative Recommendation Systems

Trust-based collaborative filtering: tackling the cold start problem using regular equivalence

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