Affiliations: [a] Computer Science and Engineering, School of Computing, MIT Art, Design and Technology University, Pune, India | [b] Department of Information Technology, Shri Vishnu Engineering College for Women, Bhimavaram, Andhra Pradesh, India | [c] Data Scientist, San Francisco, CA, USA
Correspondence:
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Corresponding author: Jagannath E. Nalavade, Computer Science and Engineering, School of Computing, MIT Art, Design and Technology University, Pune, India. E-mails: [email protected] and [email protected].
Abstract: Conventional recommendation techniques utilize various methods to compute the similarity among products and customers in order to identify the customer preferences. However, such conventional similarity computation techniques may produce incomplete information influenced by similarity measures in customers’ preferences, which leads to poor accuracy on recommendation. Hence, this paper introduced the novel and effective recommendation technique, namely Deep Embedded Clustering with matrix factorization (DEC with matrix factorization) for the collaborative recommendation. This approach creates the agglomerative matrix for the recommendation using the review data. The customer series matrix, customer series binary matrix, product series matrix, and product series binary matrix make up the agglomerative matrix. The product grouping is carried out to group the similar products using DEC for retrieving the optimal product. Moreover, the bi-level matching generates the best group customer sequence in which the relevant customers are retrieved using tversky index and angular distance. Also, the final product suggestion is made using matrix factorization, with the goal of recommending to clients the product with the highest rating. Also, according to the experimental results, the developed DEC with the matrix factorization approach produced better results with respect to f-measure values of 0.902, precision values of 0.896, and recall values of 0.908, respectively.
Keywords: Deep embedded clustering, agglomerative matrix, bilevel matching, matrix factorization, tversky index
