Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach

• URL: http://arxiv.org/abs/2204.11602v5
• Date: Sat, 24 Feb 2024 07:00:04 GMT
• Title: Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach
• Authors: Ling Huang, Can-Rong Guan, Zhen-Wei Huang, Yuefang Gao, Yingjie Kuang, Chang-Dong Wang, C. L. Philip Chen
• Abstract summary: We propose a new broad recommender system called Broad Collaborative Filtering (BroadCF) Instead of Deep Neural Networks (DNNs), Broad Learning System (BLS) is used as a mapping function to learn the complex nonlinear relationships between users and items. Extensive experiments conducted on seven benchmark datasets have confirmed the effectiveness of the proposed BroadCF algorithm.
• Score: 56.12815715932561
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Deep Neural Networks (DNNs) have been widely introduced into Collaborative Filtering (CF) to produce more accurate recommendation results due to their capability of capturing the complex nonlinear relationships between items and users.However, the DNNs-based models usually suffer from high computational complexity, i.e., consuming very long training time and storing huge amount of trainable parameters. To address these problems, we propose a new broad recommender system called Broad Collaborative Filtering (BroadCF), which is an efficient nonlinear collaborative filtering approach. Instead of DNNs, Broad Learning System (BLS) is used as a mapping function to learn the complex nonlinear relationships between users and items, which can avoid the above issues while achieving very satisfactory recommendation performance. However, it is not feasible to directly feed the original rating data into BLS. To this end, we propose a user-item rating collaborative vector preprocessing procedure to generate low-dimensional user-item input data, which is able to harness quality judgments of the most similar users/items. Extensive experiments conducted on seven benchmark datasets have confirmed the effectiveness of the proposed BroadCF algorithm

Related papers

• Frequency-aware Graph Signal Processing for Collaborative Filtering [26.317108637430664]
  We propose a frequency-aware graph signal processing method (FaGSP) for collaborative filtering. Firstly, we design a Cascaded Filter Module, consisting of an ideal high-pass filter and an ideal low-pass filter. Then, we devise a Parallel Filter Module, consisting of two low-pass filters that can easily capture the hierarchy of neighborhood.
  arXiv  Detail & Related papers  (2024-02-13T12:53:18Z)
• Neural Graph Collaborative Filtering Using Variational Inference [19.80976833118502]
  We introduce variational embedding collaborative filtering (GVECF) as a novel framework to incorporate representations learned through a variational graph auto-encoder. Our proposed method achieves up to 13.78% improvement in the recall over the test data.
  arXiv  Detail & Related papers  (2023-11-20T15:01:33Z)
• Interactive Graph Convolutional Filtering [79.34979767405979]
  Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. These problems are exacerbated by the cold start problem and data sparsity problem. Existing Multi-Armed Bandit methods, despite their carefully designed exploration strategies, often struggle to provide satisfactory results in the early stages. Our proposed method extends interactive collaborative filtering into the graph model to enhance the performance of collaborative filtering between users and items.
  arXiv  Detail & Related papers  (2023-09-04T09:02:31Z)
• Efficient Online Reinforcement Learning with Offline Data [78.92501185886569]
  We show that we can simply apply existing off-policy methods to leverage offline data when learning online. We extensively ablate these design choices, demonstrating the key factors that most affect performance. We see that correct application of these simple recommendations can provide a $mathbf2.5times$ improvement over existing approaches.
  arXiv  Detail & Related papers  (2023-02-06T17:30:22Z)
• Deep Negative Correlation Classification [82.45045814842595]
  Existing deep ensemble methods naively train many different models and then aggregate their predictions. We propose deep negative correlation classification (DNCC) DNCC yields a deep classification ensemble where the individual estimator is both accurate and negatively correlated.
  arXiv  Detail & Related papers  (2022-12-14T07:35:20Z)
• Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems [23.480069921831344]
  We develop a Collaborative Reflection-Augmented Autoencoder Network (CRANet) CRANet is capable of exploring transferable knowledge from observed and unobserved user-item interactions. We experimentally validate CRANet on four diverse benchmark datasets corresponding to two recommendation tasks.
  arXiv  Detail & Related papers  (2022-01-10T04:36:15Z)
• JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data [86.8949732640035]
  We propose JUMBO, an MBO algorithm that sidesteps limitations by querying additional data. We show that it achieves no-regret under conditions analogous to GP-UCB. Empirically, we demonstrate significant performance improvements over existing approaches on two real-world optimization problems.
  arXiv  Detail & Related papers  (2021-06-02T05:03:38Z)
• Dual-embedding based Neural Collaborative Filtering for Recommender Systems [0.7949579654743338]
  We propose a general collaborative filtering framework named DNCF, short for Dual-embedding based Neural Collaborative Filtering. In addition to learning the primitive embedding for a user (an item), we introduce an additional embedding from the perspective of the interacted items (users) to augment the user (item) representation.
  arXiv  Detail & Related papers  (2021-02-04T11:32:11Z)
• Dependency Aware Filter Pruning [74.69495455411987]
  Pruning a proportion of unimportant filters is an efficient way to mitigate the inference cost. Previous work prunes filters according to their weight norms or the corresponding batch-norm scaling factors. We propose a novel mechanism to dynamically control the sparsity-inducing regularization so as to achieve the desired sparsity.
  arXiv  Detail & Related papers  (2020-05-06T07:41:22Z)

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.