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.
|Title of host publication||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Number of pages||7|
|State||Published - 2016|
|Event||30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States|
Duration: Feb 12 2016 → Feb 17 2016
|Name||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Conference||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Period||2/12/16 → 2/17/16|
Bibliographical notePublisher Copyright:
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
ASJC Scopus subject areas
- Artificial Intelligence