Iterative collaborative filtering for recommender systems with sparse data

Zhuo Zhang, Paul Cuff, Sanjeev Kulkarni

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

3 Scopus citations

Abstract

Collaborative filtering (CF) is one of the most successful techniques in recommender systems. By utilizing co-rated items of pairwise users for similarity measurements, traditional CF uses a weighted summation to predict unknown ratings based on the available ones. However, in practice, the rating matrix is too sparse to find sufficiently many co-rated items, thus leading to inaccurate predictions. To address the case of sparse data, we propose an iterative CF that updates the similarity and rating matrix. The improved CF incrementally selects reliable subsets of missing ratings based on an adaptive parameter and therefore produces a more credible prediction based on similarity. Experimental results on the MovieLens dataset show that our algorithm significantly outperforms traditional CF, Default Voting, and SVD when the data is 1% sparse. The results also show that in the dense data case our algorithm performs as well as state of art methods.

Original languageEnglish (US)
Title of host publication2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012
DOIs
StatePublished - Dec 12 2012
Event2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012 - Santander, Spain
Duration: Sep 23 2012Sep 26 2012

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012
CountrySpain
CitySantander
Period9/23/129/26/12

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing

Keywords

  • Adaptive
  • Collaborative Filtering
  • Iterative Algorithm
  • Recommender Systems
  • Sparse Data

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  • Cite this

    Zhang, Z., Cuff, P., & Kulkarni, S. (2012). Iterative collaborative filtering for recommender systems with sparse data. In 2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012 [6349711] (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). https://doi.org/10.1109/MLSP.2012.6349711