Joint interaction with context operation for collaborative filtering

Bai, Peizhen; Ge, Yan; Liu, Fangling; Lu, Haiping

doi:10.1016/j.patcog.2018.12.003

Cited by 17 publications

(5 citation statements)

References 35 publications

(39 reference statements)

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“…Recycle learning models across areas: 1) Identify commonalities between applications, e.g., the similarity between commercial recommender systems (user-item interactions) and drug discovery (drug-target interactions); 2) Recycle models for one application to another, e.g., from recommender system [3] to drug discovery [45].…”

Section: Pykale Design 31 Green Machine Learningmentioning

confidence: 99%

PyKale

Liu

Zhou

et al. 2022

Proceedings of the 31st ACM International Conference on Information &Amp; Knowledge Management

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PyKale is a Python library for Knowledge-aware machine learning from multiple sources of data to enable/accelerate interdisciplinary research. It embodies green machine learning principles to reduce repetitions/redundancy, reuse existing resources, and recycle learning models across areas. We propose a pipeline-based application programming interface (API) so all machine learning workflows follow a standardized six-step pipeline. PyKale focuses on leveraging knowledge from multiple sources for accurate and interpretable prediction, particularly multimodal learning and transfer learning. To be more accessible, it separates code and configurations to enable non-programmers to configure systems without coding. PyKale is officially part of the PyTorch ecosystem and includes interdisciplinary examples in bioinformatics, knowledge graph, image/video recognition, and medical imaging: https://pykale.github.io/.

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Section: Pykale Design 31 Green Machine Learningmentioning

confidence: 99%

PyKale

Liu

Zhou

et al. 2022

Proceedings of the 31st ACM International Conference on Information &Amp; Knowledge Management

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“…-Recycle models for one application to another, e.g. from recommender systems [2] to drug discovery [61].…”

Section: • Recycle Learning Models Across Areasmentioning

confidence: 99%

PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python

Lu¹,

Liu²,

Turner³

et al. 2021

Preprint

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Machine learning is a general-purpose technology holding promises for many interdisciplinary research problems. However, significant barriers exist in crossing disciplinary boundaries when most machine learning tools are developed in different areas separately. We present Pykale -a Python library for knowledge-aware machine learning on graphs, images, texts, and videos to enable and accelerate interdisciplinary research. We formulate new green machine learning guidelines based on standard software engineering practices and propose a novel pipeline-based application programming interface (API). PyKale focuses on leveraging knowledge from multiple sources for accurate and interpretable prediction, thus supporting multimodal learning and transfer learning (particularly domain adaptation) with latest deep learning and dimensionality reduction models. We build PyKale on PyTorch and leverage the rich PyTorch ecosystem. Our pipeline-based API design enforces standardization and minimalism, embracing green machine learning concepts via reducing repetitions and redundancy, reusing existing resources, and recycling learning models across areas. We demonstrate its interdisciplinary nature via examples in bioinformatics, knowledge graph, image/video recognition, and medical imaging.

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“…(ii) Output: Boolean. (1) for d i in DataSet do (2) Δ digitization and normalization (3) (d i )⟶dn i � (f 1 , S 1 ), (f 2 , S 2 ), . .…”

Section: Content Filtering Based On User Preferencesmentioning

confidence: 99%

“…In general, these popular recommendation models can be divided into collaborative filtering, content-based, and hybrid approaches. e collaborative filtering method [2][3][4] is based on the view that the higher the similarity between users, the more the overlapping of user preferences. e content-based approach [5,6] is based on representations to recommend items, and these representations are usually extracted from descriptions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering

Zhang

Han

2020

Mobile Information Systems

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Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based models are representative solutions in recommender systems; however, the content-based model has some shortcomings, such as single kind of recommendation results and lack of effective perception of user preferences, while for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this paper, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.

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Joint interaction with context operation for collaborative filtering

Cited by 17 publications

References 35 publications

PyKale

PyKale

PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python

A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering

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