TY - GEN
T1 - Top-N recommender system via matrix completion
AU - Kang, Zhao
AU - Peng, Chong
AU - Cheng, Qiang
N1 - Publisher Copyright:
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2016
Y1 - 2016
N2 - Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. To do that, a nonconvex rank relaxation rather than the nuclear norm is adopted to provide a better rank approximation and an efficient optimization strategy is designed. A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level.
AB - Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. To do that, a nonconvex rank relaxation rather than the nuclear norm is adopted to provide a better rank approximation and an efficient optimization strategy is designed. A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level.
UR - https://www.scopus.com/pages/publications/85007201295
UR - https://www.scopus.com/pages/publications/85007201295#tab=citedBy
M3 - Conference contribution
AN - SCOPUS:85007201295
T3 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
SP - 179
EP - 185
BT - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
Y2 - 12 February 2016 through 17 February 2016
ER -