{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:57:51Z","timestamp":1777705071395,"version":"3.51.4"},"reference-count":10,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,1,10]]},"abstract":"<jats:p>A recommendation system serves as a distributed information filter, predicting customer preferences in reviews, ratings, and comments. Analysing customer behaviour aids in understanding needs and predicting intentions. E-commerce tracks product usage and sentiment to provide a personalized network based on consumer preference modelling. The challenge lies in optimizing item selection for suitable consumers to enhance performance. To address this, an imperative is the item recommendation approach for modelling future consumer behaviour. However, traditional machine learning methods often overlook dynamic product recommendations due to evolving user interests and changes in preferences reflected in customer ratings, causing cold-start issues. To overcome these challenges, a comprehensive deep learning approach is introduced. This approach incorporates a deep neural network for consumer preference prediction, utilizing a multi-task learning paradigm to accommodate variations in consumer ratings. The research contribution lies in applying this network to predict consumer preference scores based on latent multimodal information and item characteristics. Initially, the architecture manages changing consumer aspects and preferences by extracting features and latent factors from customer review rating data. These latent factors include customer demographic information and other concealed features that signify preferences based on experiences and behaviours. Extracted latent features are processed using a sentiment analysis model to generate embedding latent features. A finely-tuned deep neural network with hyper-parameter adjustments serves as a prediction network, forming a customer performance-oriented recommendation system. It processes embedded latent features along with associated sentiments to achieve high prediction accuracy, reliability, and latency. The deep learning architecture, enriched with consumer-specific discriminative information, generates an objective function for item recommendations with minimal error, significantly enhancing predictive performance. Empirical experiments on Amazon review datasets validate the proposed model\u2019s performance, showcasing its enhanced effectiveness and scalability in handling substantial data volumes.<\/jats:p>","DOI":"10.3233\/jifs-231116","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T11:27:23Z","timestamp":1700220443000},"page":"1341-1357","source":"Crossref","is-referenced-by-count":11,"title":["Deep learning architecture towards consumer buying behaviour prediction using multitask learning paradigm"],"prefix":"10.1177","volume":"46","author":[{"given":"M.P.","family":"Geetha","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India"}]},{"given":"D.","family":"Karthika Renuka","sequence":"additional","affiliation":[{"name":"Department of Information Technology, PSG College of Technology, Coimbatore, Tamil Nadu, India"}]}],"member":"179","reference":[{"issue":"13","key":"10.3233\/JIFS-231116_ref4","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1016\/j.patrec.2008.06.010","article-title":"Incremental Bayesian Classification for Multivariate Normal Distribution Data","volume":"29","author":"Agrawal","year":"2008","journal-title":"Pattern Recognition Letters"},{"issue":"1","key":"10.3233\/JIFS-231116_ref8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1644873.1644874","article-title":"Factor in the neighbors: Scalable and accurate collaborative filtering","volume":"4","author":"Koren","year":"2010","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"issue":"9","key":"10.3233\/JIFS-231116_ref9","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1109\/TKDE.2007.1057","article-title":"A Low- Granularity Classifier for Data Streams with Concept Drifts and Biased Class Distribution","volume":"19","author":"Wang","year":"2007","journal-title":"IEEE Trans. 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