Discrete Content-aware Matrix Factorization

Lian, Defu; Liu, Rui; Ge, Yong; Zheng, Kai; Xie, Xing; Cao, Longbing

doi:10.1145/3097983.3098008

Cited by 68 publications

(47 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is consistent with the findings in [Liu et al, 2014] that the direct discrete optimization is stronger than two-stage approaches and that side information makes the user codes and item codes more representative, which can boost the performance of recommendation. However, the rather small performance gap between DCF and DCMF indicates that DCMF fails to make full use of the side information.…”

Section: Performance Comparison (Rq1)supporting

confidence: 91%

“…Similar to the idea of projection, [Zhou and Zha, 2012] generate binary code from rotated continuous user-item latent factors by running ITQ [Gong and Lazebnik, 2011]. In order to derive more compact binary codes, [Liu et al, 2014] imposed the de-correlation constraint of different binary codes on continuous user-item latent factors and then rounded them to produce binary codes. However, [Zhang et al, 2014] argued that hashing only preserves similarity between user and item rather than inner product based preference, so subsequent hashing may harm the accuracy of preference predictions, thus they imposed a Constant Feature Norm(CFN) constraint on user-item continuous latent factors, and then quantized similarities by respectively thresholding their magnitudes and phases.…”

Section: Efficient Recommendationmentioning

confidence: 99%

“…a) Yelp. This dataset [Lian et al, 2017] originally contains 409,117 users, 85,539 items (points of interest on Yelp such as restaurants and hotels), and 2,685,066 ratings with integer scores ranging from 1 to 5. Besides, each item has a set of textual reviews posted by the users.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…This is the state-of-the-art binarized method for CF with side information [Lian et al, 2017]. It extends DCF by encoding the side features as the constraints for user codes and item codes.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…As we exactly follow the experimental settings of [Lian et al, 2017], we refer to their optimal settings for hyper-parameters of DCMF, DCF, and BCCF.…”

Section: Experimental Settingsmentioning

confidence: 99%

See 4 more Smart Citations

Discrete Factorization Machines for Fast Feature-based Recommendation

Liu

Feng

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 10 7 , results in expensive storage and computational cost. This prohibits fast recommendation especially on mobile applications where the computational resource is very limited. In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation. DFM binarizes the real-valued model parameters (e.g., float32) of every feature embedding into binary codes (e.g., boolean), and thus supports efficient storage and fast user-item score computation. To avoid the severe quantization loss of the binarization, we propose a convergent updating rule that resolves the challenging discrete optimization of DFM. Through extensive experiments on two realworld datasets, we show that 1) DFM consistently outperforms state-of-the-art binarized recommendation models, and 2) DFM shows very competitive performance compared to its real-valued version (FM), demonstrating the minimized quantization loss.

show abstract

Section: Performance Comparison (Rq1)supporting

confidence: 91%

Section: Efficient Recommendationmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

“…As we exactly follow the experimental settings of [Lian et al, 2017], we refer to their optimal settings for hyper-parameters of DCMF, DCF, and BCCF.…”

Section: Experimental Settingsmentioning

confidence: 99%

See 3 more Smart Citations

Discrete Factorization Machines for Fast Feature-based Recommendation

Liu

Feng

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

Point of Interest Recommendation Method Based on Bidirectional Matrix and Deep Belief Network

Song

et al. 2023

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Point‐of‐interest (POI) recommendation has always faced serious data sparsity problem. Most of the existing POI recommendation methods do not consider the bidirectional relationship between the user‐preference for locations and the location‐ attractiveness to users in the original dataset when solving the sparsity. For this problem, a point‐of‐interest recommendation method based on bidirectional matrix and deep belief network (POI_DBNBM) is proposed in this paper. Specially, from the perspectives of users and locations, we first construct two different matrices with bidirectional relationship by mapping the original dataset, and we use deep belief network (DBN) with strong learning ability to predict the two matrices. Then, a local–global weight optimization method is developed to weight and fuse the two result matrices obtained by DBN, so as to achieve the more precise user's rating prediction matrix for locations. The abundant experimental studies and evaluations are conducted on FourSquare dataset. Results are encouraging and better than those previously reported on the dataset. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

show abstract

Deep Tag Recommendation Based on Discrete Tensor Factorization

Qin

2018

Neural Information Processing

View full text Add to dashboard Cite

Discrete Content-aware Matrix Factorization

Cited by 68 publications

References 21 publications

Discrete Factorization Machines for Fast Feature-based Recommendation

Discrete Factorization Machines for Fast Feature-based Recommendation

Point of Interest Recommendation Method Based on Bidirectional Matrix and Deep Belief Network

Deep Tag Recommendation Based on Discrete Tensor Factorization

Contact Info

Product

Resources

About