Enhanced PMF Model to Predict User Interest for Web API Recommendation

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Author(s): T. Ramathulasi (Vellore Institute of Technology, India)and M. Rajasekhar Babu (Vellore Institute of Technology, India)
Copyright: 2022
Pages: 9
Source title: Handbook of Research on Advances in Data Analytics and Complex Communication Networks
Source Author(s)/Editor(s): P. Venkata Krishna (Sri Padmavati Mahila University, India)
DOI: 10.4018/978-1-7998-7685-4.ch009

Keywords: Data Analysis and Statistics / Information Science Reference / Media & Communications / Wireless Systems

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Abstract

Many methods focus solely on the relationship between the API and the user and fail to capture their contextual value. Because of this, they could not get better accuracy. The accuracy of the API recommendation can be improved by considering the effect of API contextual information on their latent attribute and the effect of the user time factor on the latent attribute of the user through the deep learning-based matrix factorization method (DL-PMF). In this chapter, a CNN (convolutional neural network) with an attention mechanism for the hidden features of web API elements and an LSTM (long-term and short-term memory) network is introduced to find the hidden features of service users. Finally, the authors combined PMF (probabilistic matrix factorization) to estimate the value of the recommended results. Experimental results obtained by the DL-PMF method show better than the experimental results obtained by the PMF and the ConvMF (convolutional matrix factorization) method in the recommended accuracy.

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Enhanced PMF Model to Predict User Interest for Web API Recommendation

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