TY - GEN
T1 - Iterative collaborative filtering for recommender systems with sparse data
AU - Zhang, Zhuo
AU - Cuff, Paul
AU - Kulkarni, Sanjeev
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Adaptive
KW - Collaborative Filtering
KW - Iterative Algorithm
KW - Recommender Systems
KW - Sparse Data
UR - http://www.scopus.com/inward/record.url?scp=84870668090&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84870668090&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2012.6349711
DO - 10.1109/MLSP.2012.6349711
M3 - Conference contribution
AN - SCOPUS:84870668090
SN - 9781467310260
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
BT - 2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012
T2 - 2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012
Y2 - 23 September 2012 through 26 September 2012
ER -