Regularizing Matrix Factorization with Implicit User Preference Embeddings for Web API Recommendation

Fletcher, Kenneth K.

doi:10.1109/scc.2019.00014

Cited by 37 publications

(14 citation statements)

References 20 publications

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“…Furthermore, the CF algorithm applies a specific item based on each user group. It has been confirmed that better performance than neighborbased CF can be achieved with MF model-based CF, with the authors attempting to assess user preferences when presenting invitations in [25] and terms of APIs and mashups. This assessment was made possible by the authors incorporating user preferences into the MF model to create a new recommendation model.…”

Section: Related Workmentioning

confidence: 96%

Assessing User Interest in Web API Recommendation using Deep Learning Probabilistic Matrix Factorization

Ramathulasi¹,

Babu²

2023

IJACSA

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Internet 2.0 Things connected to the Internet not only manage data supply through devices but also control the commands that flow through it. The communication technology created by the desired sensor is used by a new computing model so that the collected data appears in Web 2.0 for management. In addition to enhancing Sense efficiency through the simple IoT computing process, it is used in many cases for example video surveillance, and improved and intelligent manufacturing. Every fragment of the system is carefully continued and supervised in this process by software collection using a large number of recurs. An important process for this is to access web APIs from various public platforms in an efficient way. The use of different APIs by developers for the integration of different IoT devices and the deployment process required for this is unnecessary. Obtaining configured target APIs makes it easy to know where and how to get started with the workflow approach. Rapid industrial development can be achieved through this powerful API approach. But finding adequately powerful APIs from a large number of APIs has become a great challenge. However, due to the massive spike in the count of APIs, combining the two APIs has now become a major challenge. In this paper, for the time being, only the relationships between users and the API are considered. In this case, they had to face difficulties in extracting contextual value from their interpretation. So better accuracy could not be obtained due to this. The consequence of the user's time aspect on the cryptographic properties concerning the information collected from the API contextual description can be enhanced by the Deep Learning Probabilistic Matrix Factorization (DL-PMF) method, which improves the accuracy of the API recommendation in considering the cryptographic features of the user in the API recommendation. In this paper, we have used CNN (Convulsive Neural Network) for web elements such as APIs, and LSTM (Long-Term and Short-Term Memory) Network, which works with a diligent mechanism to find hidden features, to find hidden features that suit the tastes of the users. In conclusion, the combination of PMF (Probabilistic Matrix Factorization) evaluation of the recommended results was obtained as described above. The combination of DL-PMF method experimental results was found to be better than previous PMF, ConvMF, and other methods, thus improving the recommended accuracy.

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Section: Related Workmentioning

confidence: 96%

Assessing User Interest in Web API Recommendation using Deep Learning Probabilistic Matrix Factorization

Ramathulasi¹,

Babu²

2023

IJACSA

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“…The performance of the MF model has been verified to be superior to neighborhood-based CF, especially in cases where the data are sparse. In paper [28], the authors tried to learn the preferences that are displayed by users during the invocation of APIs, mashups, and users. The authors created a new recommendation model by combining embedded user preference and the MF model.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective

Gao

Qin

Barroso

et al. 2022

IEEE Trans. Emerg. Top. Comput. Intell.

108

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“…Yao et al (2015) propose a CF method that integrates implicit API correlations regularisation and matrix factorisation for API recommendation. Fletcher (2019) incorporates the user's implicit preferences (i.e. invocation history) into a matrix factorisation model to improve the accuracy and diversity of recommendations.…”

Section: Related Workmentioning

confidence: 99%

API recommendation for Mashup creation based on neural graph collaborative filtering

Lian

Tang

2021

Connection Science

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With the increase of open APIs appeared on the Web, reusing or combining these APIs to develop novel applications (e.g. Mashups) has attracted great interest from developers. However, to quickly find a suitable one among a huge number of APIs to meet a developer's requirement is basically a non-trivial issue. Therefore, a highquality API recommendation system is desirable. Although a number of collaborative filtering methods have been proposed for API recommendation, their recommendation accuracy is limited and needs to be further improved. Based on the neural graph collaborative filtering technique, this paper proposes an API recommendation method that exploits the high-order connectivity between APIs and API users. To evaluate the proposed method, extensive experiments are conducted on a real API dataset and the results show that the proposed method outperforms the state-of-the-art methods in API recommendation.

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Regularizing Matrix Factorization with Implicit User Preference Embeddings for Web API Recommendation

Cited by 37 publications

References 20 publications

Assessing User Interest in Web API Recommendation using Deep Learning Probabilistic Matrix Factorization

Assessing User Interest in Web API Recommendation using Deep Learning Probabilistic Matrix Factorization

Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective

API recommendation for Mashup creation based on neural graph collaborative filtering

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