DeepRec: An Open-source Toolkit for Deep Learning based Recommendation

Zhang, Shuai; Tay, Yi; Yao, Lina; Wu, Bin; Sun, Aixin

doi:10.24963/ijcai.2019/963

Cited by 17 publications

(6 citation statements)

References 1 publication

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“…Recently, deep neural networks (DNN) [46]-based approaches to an RS attract researchers' attention [47][48][49][50]. Zhang et al conduct a detailed review of DNN-based RSs [51].…”

Section: B Related Workmentioning

confidence: 99%

An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems

Shang

Luo

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

102

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A recommender system is highly efficient in filtering people's desired information from high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach becomes highly popular when implementing a recommender system. However, current LF models mostly adopt single distance-oriented Loss like an L 2 norm-oriented one, which ignores target data's characteristics described by other metrics like an L 1 norm-oriented one. To investigate this issue, this paper proposes an L 1 -and-L 2 -norm-oriented Latent Factor (L 3 F) model. It adopts two-fold ideas: a) aggregating L 1 norm's robustness and L 2 norm's stability to form its Loss, and b) adaptively adjusting weights of L 1 and L 2 norms in its Loss. By doing so, it achieves fine aggregation effects with L 1 norm-oriented Loss's robustness and L 2 norm-oriented Loss's stability to precisely describe HiDS data with outliers. Experimental results on nine HiDS datasets generated by real systems show that an L 3 F model significantly outperforms state-of-the-art models in prediction accuracy for missing data of an HiDS dataset. Its computational efficiency is also comparable with the most efficient LF models. Hence, it has good potential for addressing HiDS data from real applications.

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“…Recently, deep neural networks (DNN) [46]-based approaches to an RS attract researchers' attention [47][48][49][50]. Zhang et al conduct a detailed review of DNN-based RSs [51].…”

Section: B Related Workmentioning

confidence: 99%

An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems

Shang

Luo

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

102

View full text Add to dashboard Cite

show abstract

“…Several software frameworks have been released to help address these issues, providing algorithm implementations, as well as evaluation procedures and metrics. This is the case of libraries such as Beta-Recsys [71], Cornac [88], DaisyRec [99], DeepRec [108], Elliot [3], LensKit [28], LibRec [43], LightFM [61], MyMediaLite [37], Open-Rec [105], RankSys [101] or Surprise [51]. Differently from our framework, these libraries have been developed for the general item recommendation case.…”

Section: Related Frameworkmentioning

confidence: 99%

RELISON: A Framework for Link Recommendation in Social Networks

Sanz-Cruzado

Castells

2022

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Link recommendation is an important and compelling problem at the intersection of recommender systems and online social networks. Given a user, link recommenders identify people in the platform the user might be interested in interacting with. We present RELISON, an extensible framework for running link recommendation experiments. The library provides a wide range of algorithms, along with tools for evaluating the produced recommendations. RELISON includes algorithms and metrics that consider the potential effect of recommendations on the properties of online social networks. For this reason, the library also implements network structure analysis metrics, community detection algorithms, and network diffusion simulation functionalities. The library code and documentation is available at https://github.com/ir-uam/RELISON.

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“…2) Benchmarking tools. With the evolution of deep learning-based recommendation techniques, many useful open-source libraries and tools have been developed, such as DeepRec [121], daisyRec [96], NeuRec [109], RecBole [123], DeepCTR [7], FuxiCTR [9], TensorFlow Recommenders [14], TorchRec [15], PaddleRec [13], and EasyRec [8], each providing tens of popular recommendation algorithms 1 . 3) Benchmarking results.…”

Section: Introductionmentioning

confidence: 99%

BARS: Towards Open Benchmarking for Recommender Systems

Zhu¹,

Mao²,

Dai³

et al. 2022

Preprint

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The past two decades have witnessed the rapid development of personalized recommendation techniques. Despite the significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field. Many of the existing studies perform model evaluations and comparisons in an ad-hoc manner, for example, by employing their own private data splits or using a different experimental setting. However, such conventions not only increase the difficulty in reproducing existing studies, but also lead to inconsistent experimental results among them. This largely limits the credibility and practical value of research results in this field. To tackle these issues, we present an initiative project aimed for open benchmarking for recommender systems. In contrast to some earlier attempts towards this goal, we take one further step by setting up a standardized benchmarking pipeline for reproducible research, which integrates all the details about datasets, source code, hyper-parameter settings, running logs, and evaluation results. The benchmark is designed with comprehensiveness and sustainability in mind. It spans both matching and ranking tasks, and also allows anyone to easily follow and contribute. We believe that our benchmark could not only reduce the redundant efforts of researchers to re-implement or re-run existing baselines, but also drive more solid and reproducible research on recommender systems.

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DeepRec: An Open-source Toolkit for Deep Learning based Recommendation

Cited by 17 publications

References 1 publication

An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems

An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems

RELISON: A Framework for Link Recommendation in Social Networks

BARS: Towards Open Benchmarking for Recommender Systems

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DeepRec: An Open-source Toolkit for Deep Learning based Recommendation

Cited by 17 publications

References 1 publication

An L 1-and-L 2-Norm-Oriented Latent Factor Model for Recommender Systems

An L 1-and-L 2-Norm-Oriented Latent Factor Model for Recommender Systems

RELISON: A Framework for Link Recommendation in Social Networks

BARS: Towards Open Benchmarking for Recommender Systems

Contact Info

Product

Resources

About

An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems

An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems