HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation

• URL: http://arxiv.org/abs/2205.12042v1
• Date: Tue, 24 May 2022 12:51:52 GMT
• Title: HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation
• Authors: Fan Wang, Weiming Liu, Chaochao Chen, Mengying Zhu, Xiaolin Zheng
• Abstract summary: Hash-based collaborative filtering (Hash-CF) approaches employ efficient Hamming distance of learned binary representations of users and items to accelerate recommendations. We propose HCFRec, a novel Hash-CF approach for effective and efficient recommendations.
• Score: 23.73674947905047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ever-increasing data scale of user-item interactions makes it challenging for an effective and efficient recommender system. Recently, hash-based collaborative filtering (Hash-CF) approaches employ efficient Hamming distance of learned binary representations of users and items to accelerate recommendations. However, Hash-CF often faces two challenging problems, i.e., optimization on discrete representations and preserving semantic information in learned representations. To address the above two challenges, we propose HCFRec, a novel Hash-CF approach for effective and efficient recommendations. Specifically, HCFRec not only innovatively introduces normalized flow to learn the optimal hash code by efficiently fit a proposed approximate mixture multivariate normal distribution, a continuous but approximately discrete distribution, but also deploys a cluster consistency preserving mechanism to preserve the semantic structure in representations for more accurate recommendations. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our HCFRec compared to the state-of-art methods in terms of effectiveness and efficiency.

Related papers

• Efficient and Robust Regularized Federated Recommendation [52.24782464815489]
  The recommender system (RSRS) addresses both user preference and privacy concerns. We propose a novel method that incorporates non-uniform gradient descent to improve communication efficiency. RFRecF's superior robustness compared to diverse baselines.
  arXiv  Detail & Related papers  (2024-11-03T12:10:20Z)
• Adversarial Collaborative Filtering for Free [27.949683060138064]
  Collaborative Filtering (CF) has been successfully used to help users discover the items of interest. Existing methods suffer from noisy data issue, which negatively impacts the quality of recommendation. We present Sharpness-aware Collaborative Filtering (CF), a simple yet effective method that conducts adversarial training without extra computational cost over the base.
  arXiv  Detail & Related papers  (2023-08-20T19:25:38Z)
• Improved Distribution Matching for Dataset Condensation [91.55972945798531]
  We propose a novel dataset condensation method based on distribution matching. Our simple yet effective method outperforms most previous optimization-oriented methods with much fewer computational resources.
  arXiv  Detail & Related papers  (2023-07-19T04:07:33Z)
• CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashing [42.67510119856105]
  generative models, such as Generative Adversarial Networks (GANs), can generate synthetic data in an image hashing model. GANs are difficult to train, which prevents hashing approaches from jointly training the generative models and the hash functions. We propose a novel framework, the generative cooperative hashing network, which is based on energy-based cooperative learning.
  arXiv  Detail & Related papers  (2022-10-09T15:42:36Z)
• Late Fusion Multi-view Clustering via Global and Local Alignment Maximization [61.89218392703043]
  Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Most of existing approaches directly fuse multiple pre-specified similarities to learn an optimal similarity matrix for clustering. We propose late fusion MVC via alignment to address these issues.
  arXiv  Detail & Related papers  (2022-08-02T01:49:31Z)
• Distributed Dynamic Safe Screening Algorithms for Sparse Regularization [73.85961005970222]
  We propose a new distributed dynamic safe screening (DDSS) method for sparsity regularized models and apply it on shared-memory and distributed-memory architecture respectively. We prove that the proposed method achieves the linear convergence rate with lower overall complexity and can eliminate almost all the inactive features in a finite number of iterations almost surely.
  arXiv  Detail & Related papers  (2022-04-23T02:45:55Z)
• FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal Retrieval [41.125141897096874]
  Cross-modal hashing is favored for its effectiveness and efficiency. Most existing methods do not sufficiently exploit the discriminative power of semantic information when learning the hash codes. We propose Fast Discriminative Discrete Hashing (FDDH) approach for large-scale cross-modal retrieval.
  arXiv  Detail & Related papers  (2021-05-15T03:53:48Z)
• CIMON: Towards High-quality Hash Codes [63.37321228830102]
  We propose a new method named textbfComprehensive stextbfImilarity textbfMining and ctextbfOnsistency leartextbfNing (CIMON) First, we use global refinement and similarity statistical distribution to obtain reliable and smooth guidance. Second, both semantic and contrastive consistency learning are introduced to derive both disturb-invariant and discriminative hash codes.
  arXiv  Detail & Related papers  (2020-10-15T14:47:14Z)
• Deep Hashing with Hash-Consistent Large Margin Proxy Embeddings [65.36757931982469]
  Image hash codes are produced by binarizing embeddings of convolutional neural networks (CNN) trained for either classification or retrieval. The use of a fixed set of proxies (weights of the CNN classification layer) is proposed to eliminate this ambiguity. The resulting hash-consistent large margin (HCLM) proxies are shown to encourage saturation of hashing units, thus guaranteeing a small binarization error.
  arXiv  Detail & Related papers  (2020-07-27T23:47:43Z)
• Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation [31.69262747122024]
  We propose a fast cold-start recommendation method, called Multi-Feature Discrete Collaborative Filtering (MFDCF) Specifically, a low-rank self-weighted multi-feature fusion module is designed to adaptively project the multiple content features into binary hash codes. MFDCF outperforms the state-of-the-arts on various aspects.
  arXiv  Detail & Related papers  (2020-03-24T08:55:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.