Discrete content-aware matrix factorization

Publication Type:
Conference Proceeding
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, Part F129685 pp. 325 - 334
Issue Date:
Filename Description Size
p325-lian.pdfPublished version902.51 kB
Adobe PDF
Full metadata record
© 2017 ACM. Precisely recommending relevant items from massive candidates to a large number of users is an indispensable yet computationally expensive task in many online platforms (e.g., Amazon.com and Netfix.com). A promising way is to project users and items into a Hamming space and then recommend items via Hamming distance. However, previous studies didn't address the cold-start challenges and couldn't make the best use of preference data like implicit feedback. To fill this gap, we propose a Discrete Content-aware Matrix Factorization (DCMF) model, 1) to derive compact yet informative binary codes at the presence of user/item content information; 2) to support the classification task based on a local upper bound of logit loss; 3) to introduce an interaction regularization for dealing with the sparsity issue. We further develop an eficient discrete optimization algorithm for parameter learning. Based on extensive experiments on three real-world datasets, we show that DCFM outperforms the state-of-the-arts on both regression and classification tasks.
Please use this identifier to cite or link to this item: