LCNA-LSTM CNN based attention model for recommendation system to improve marketing strategies on e-commerce
Abstract
E-commerce industries have grown at an unpredicted and unprecedented rate in the 21st century, booming in the midst of the COVID-19 catastrophe and revolutionizing our way of life. The COVID-19 pandemic has demonstrated the widespread strong uptake of e-commerce. In today scenario, success of any business depends upon e-commerce platform. As e-commerce uses Machine learning algorithms for processing but they also encounter serious issues, i.e., cold start, sparsity, scalability and many more. In this research work, researchers address the cold start issue, as efficiency drops due to new users or lower engagement of users. Same is resolved in proposed LSTM CNN Based Attention Model (LCNA), a Longest short-term memory (LSTM) recurring neural networks, Convolution Neural Network (CNN) and deep attention layer based model for collaborative filtering to solve the problem of cold start for a new user. The proposed model in this study uses deep attention layer for semantic ranking with cosine similarity to improve the recommendation. Proposed framework primarily functions in stages, starting with the creation of interactive map matrices, then improving ranking using CNN, LSTM, and deep attention layer, and concluding with the framework’s prediction of three key metrics: mean absolute error (MAE), root MSE (RMSE), and accuracy. The framework is put to the test in several metrices using various recommender metrics on the electronics dataset from the Amazon dataset.
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DOI: https://doi.org/10.32629/jai.v7i1.972
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Copyright (c) 2023 Vikas Sethi, Rajneesh Kumar, Stuti Mehla, Anju Bhandari Gandhi, Shally Nagpal, Sumit Rana
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