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Learning Discrete Hashing Towards Efficient Fashion Recommendation

Luyao Liu, Xingzhong Du, Lei Zhu, Fumin Shen, Zi Huang
2018 Data Science and Engineering  
To address the problem, in this paper, we propose a new model called Discrete Supervised Fashion Coordinates Hashing.  ...  is discretized into a fixed-length binary vector.  ...  Methodology In this section, we detail our proposed Discrete Supervised Fashion Coordinates Hashing (DSFCH) for efficient fashion recommendation. We develop a unified hashing learning framework.  ... 
doi:10.1007/s41019-018-0079-z fatcat:6i73qjgdovdddnyff3vnsgjbbu

Learning to Hash with Graph Neural Networks for Recommender Systems [article]

Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, Xia Hu
2020 arXiv   pre-print
learn continuous and discrete codes.  ...  In this work, we investigate the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and propose a simple yet effective discrete representation learning framework to jointly  ...  To the best of our knowledge, this paper represents the first effort towards this target in recommendation. • A simple discrete optimization strategy is adapted to optimize the parameters, which facilitates  ... 
arXiv:2003.01917v1 fatcat:wpexdptpeze7tdumqb6r4lhn7u

LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization [article]

Honglei Zhang, Fangyuan Luo, Jun Wu, Xiangnan He, Yidong Li
2022 arXiv   pre-print
Moreover, we devise an efficient federated discrete optimization algorithm to collaboratively train model parameters between the server and clients, which can effectively prevent real-valued gradient attacks  ...  To this end, we propose a lightweight federated recommendation framework with privacy-preserving matrix factorization, LightFR, that is able to generate high-quality binary codes by exploiting learning  ...  towards item binary matrix.  ... 
arXiv:2206.11743v1 fatcat:otr5aypnpvagpe4e3hq3stx5xe

Discrete Collaborative Filtering

Hanwang Zhang, Fumin Shen, Wei Liu, Xiangnan He, Huanbo Luan, Tat-Seng Chua
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
We address the efficiency problem of Collaborative Filtering (CF) by hashing users and items as latent vectors in the form of binary codes, so that user-item affinity can be efficiently calculated in a  ...  However, existing hashing methods for CF employ binary code learning procedures that most suffer from the challenging discrete constraints.  ...  To this end, we are interested in hashing users and items into binary codes for efficient recommendation since the useritem similarity search can be efficiently conducted in Hamming space.  ... 
doi:10.1145/2911451.2911502 dblp:conf/sigir/ZhangSLHLC16 fatcat:47j23aqxzve5xfxsmabsf7tuqu

Discrete Factorization Machines for Fast Feature-based Recommendation [article]

Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang
2018 arXiv   pre-print
., float32) of every feature embedding into binary codes (e.g., boolean), and thus supports efficient storage and fast user-item score computation.  ...  In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.  ...  Related Work We first review efficient recommendation algorithms using latent factor models, and then discuss recent advance in discrete hashing techniques.  ... 
arXiv:1805.02232v3 fatcat:cwekaftc6rc4hkjut7bbxpjxom

Discrete Factorization Machines for Fast Feature-based Recommendation

Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
., float32) of every feature embedding into binary codes (e.g., boolean), and thus supports efficient storage and fast user-item score computation.  ...  In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.  ...  Related Work We first review efficient recommendation algorithms using latent factor models, and then discuss recent advance in discrete hashing techniques.  ... 
doi:10.24963/ijcai.2018/479 dblp:conf/ijcai/Liu0FNLZ18 fatcat:o4i62ez52zdqlepreyzuo7suq4

Representation Learning for Efficient and Effective Similarity Search and Recommendation [article]

Casper Hansen
2021 arXiv   pre-print
A common approach is to represent data objects as binary vectors, denoted hash codes, which require little storage and enable efficient similarity search through direct indexing into a hash table or through  ...  The contributions are empirically validated on several tasks related to similarity search and recommendation.  ...  codes towards more efficient multi-index hashing search.  ... 
arXiv:2109.01815v1 fatcat:tlq2uweeebde5gmi56ubm6rttm

Semi-supervised Network Embedding with Differentiable Deep Quantisation [article]

Tao He, Lianli Gao, Jingkuan Song, Yuan-Fang Li
2021 arXiv   pre-print
while being far more space- and time-efficient.  ...  on four real-world networks of diverse characteristics shows that d-SNEQ outperforms a number of state-of-the-art embedding methods in link prediction, path prediction, node classification, and node recommendation  ...  We compare the efficiency of the continuous and discrete embeddings and we choose SH as the representative hash method.  ... 
arXiv:2108.09128v1 fatcat:ix7x3rk5fvfuppjn4rsltylssa

On Practical Discrete Gaussian Samplers for Lattice-Based Cryptography

James Howe, Ayesha Khalid, Ciara Rafferty, Francesco Regazzoni, Maire O'Neill
2018 IEEE transactions on computers  
Hashing is used to further narrow down the binary search time resulting in an area-efficient and yet high average throughput performance.  ...  For these reasons, hardware discrete Ziggurat samplers are not recommended and are not included in Figure 7 .  ... 
doi:10.1109/tc.2016.2642962 fatcat:lbhr743bd5bgnk77y3z4z3qtsu

Unsupervised t-Distributed Video Hashing and Its Deep Hashing Extension

Yanbin Hao, Tingting Mu, John Y. Goulermas, Jianguo Jiang, Richang Hong, Meng Wang
2017 IEEE Transactions on Image Processing  
The efficiency and effectiveness of the proposed methods are evaluated on two public video collections via comparisons against multiple classical and state-of-the-art methods.  ...  learning, t-USMVH combines multiple types of feature representations and effectively fuses them by examining a continuous relevance score based on a Gaussian estimation over pairwise distances, and also a discrete  ...  To improve the hashing performance, multi-view hashing techniques have also been developed to learn compact and efficient binary hash codes from a mixture of multiple feature views [15] , [26] , [36  ... 
doi:10.1109/tip.2017.2737329 pmid:28796619 fatcat:dp27jriy5ba27hp6gthykjebeu

Formal Security Proofs for a Signature Scheme with Partial Message Recovery [chapter]

Daniel R. L. Brown, Don B. Johnson
2001 Lecture Notes in Computer Science  
The proof works in the random oracle model (which assumes an ideal hash function) combined with an ideal cipher model.  ...  This article gives a formal proof of the security of PVSSR, which reduces the difficulty of existential forgery to the difficulty of the discrete logarithm problem.  ...  We recommend using a 20-byte elliptic curve (160 bits or 163 bits), SHA-1 as a good 20-byte hash function, and 3DES.  ... 
doi:10.1007/3-540-45353-9_11 fatcat:yczqo5e2mnbkbkywxuh3nxlr7i

Towards a Video Passive Content Fingerprinting Method for Partial-Copy Detection Robust against Non-Simulated Attacks

Zobeida Jezabel Guzman-Zavaleta, Claudia Feregrino-Uribe, Houbing Song
2016 PLoS ONE  
Additionally, we use an efficient multilevel filtering system accelerating the processes of fingerprint extraction and matching.  ...  Motivated by these issues, in this work we propose a content fingerprinting method based on the extraction of a set of independent binary global and local fingerprints.  ...  We have also explored the use of light-weight global and local binary descriptors to achieve computational efficiency.  ... 
doi:10.1371/journal.pone.0166047 pmid:27861492 pmcid:PMC5115698 fatcat:xqgulrbf65c7bhgh2pwumo5jd4

Anchor Transform: Learning Sparse Embeddings for Large Vocabularies [article]

Paul Pu Liang, Manzil Zaheer, Yuan Wang, Amr Ahmed
2021 arXiv   pre-print
In this paper, we design a simple and efficient embedding algorithm that learns a small set of anchor embeddings and a sparse transformation matrix.  ...  On text classification, language modeling, and movie recommendation benchmarks, we show that ANT is particularly suitable for large vocabulary sizes and demonstrates stronger performance with fewer parameters  ...  In this work, we break this barrier and, to the best of our knowledge, are the first to integrate IBP with deep representation learning of discrete objects by employing an efficient SVA based inference  ... 
arXiv:2003.08197v4 fatcat:7yfdwpflwfcndjfrqa6ldqp6ue

Using hash tables to manage the time-storage complexity in a point location problem: Application to explicit model predictive control

Farhad Bayat, Tor Arne Johansen, Ali Akbar Jalali
2011 Automatica  
Utilizing the concept of hash tables and the associated hash functions, the proposed method solves an aggregated point location problem that overcomes prohibitive complexity growth with the number of polyhedral  ...  The first-stage subproblems are solved efficiently, supported by one-dimensional hash tables, as an alternative to, for example, hyperrectangles organized in a binary search tree (Canale et al., 2009;  ...  Efficient point location: main idea The main idea is to solve n x one-dimensional subproblems and then aggregate the results using a discrete set intersection method to solve the original problem.  ... 
doi:10.1016/j.automatica.2011.01.009 fatcat:h3ajev7l7bc2dcezrq7b4ibe5q

Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception

Ruofei Ouyang, Bryan Kian Hsiang Low
2019 Autonomous Robots  
Systems Zihao Xiao*, Jun Zhu, Jianfei Chen Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework Yanzhi Wang, Caiwen Ding, Geng  ...  Systems Amrita Saha*, Mitesh Khapra, Karthik Sankaranarayanan Towards efficient detection of overlapping communities in massive networks Bingjie Sun*, Huawei Shen, Jinhua Gao, Wentao Ouyang, Xueqi Cheng  ... 
doi:10.1007/s10514-018-09826-z fatcat:67yqhwmgozccxni56rxmuapjgm
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