The recommender system is the most profound research area for e-commerce product recommendations. Currently, many e-commerce platforms use a text-based product search, which has limitations to fetch the most similar products. An image-based similarity search for recommendations had considerable gains in popularity for many areas, especially for the e-commerce platforms giving a better visual search experience by the users. In our research work, we proposed a machine-learning-based approach for a similar image-based recommender system. We applied a dimensionality reduction technique using Principal Component Analysis (PCA) through Singular Value Decomposition (SVD) for transforming the extracted features into lower-dimensional space. Further, we applied the K-Means++ clustering approach for the possible cluster identification for a similar group of products. Later, we computed the Manhattan distance measure for the input image to the target clusters set for fetching the top-N similar products with low distance measure. We compared our approach with five different unsupervised clustering algorithms, namely Minibatch, K-Mediod, Agglomerative, Brich, and the Gaussian Mixture Model (GMM), and used the 40,000 fashion product image dataset from the Kaggle web platform for the product recommendation process. We computed various cluster performance metrics on K-means++ and achieved a Silhouette Coefficient (SC) of 0.1414, a Calinski-Harabasz (CH) index score of 669.4, and a Davies–Bouldin (DB) index score of 1.8538. Finally, our proposed PCA-SVD transformed K-mean++ approach showed superior performance compared to the other five clustering approaches for similar image product recommendations.

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<span>Visual similarity recommendations have an immense role in E-commerce portals. Fetching the appropriate similar products and suggesting to the buyers based on the product image's visual features is complex. Here in our research, we presented an efficient E-commerce similar product network model (e-SimNet) for visually similar recommendations. To achieve our objective, we have performed image feature extraction and generating embeddings using deep learning techniques and built an Index tree using the approximate nearest neighbor oh yeah (ANNOY) algorithm. Further, we have fetches top-N the near similar items using distance measure. We have benchmarked our model in terms of accuracy, error rate, and results show that better than other state-of-the-art approaches with 96.22% of accuracy.</span>

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