Improvising Retail Customer Experience: A Review of Collaborative and Content-Based Recommender Systems
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I1P116Keywords:
Recommender Systems, Content-Based Filtering, Retail, Collaborative Filtering, Customer Experience, Hybrid Models, Deep LearningAbstract
Recommender systems (RSs) have become pillars of personalization in contemporary retail that is improving customer experience and business value. With the help of sophisticated algorithms, retailers will be able to offer personalized product recommendations taking into account the personal preferences of consumers and thus minimizing the information overload and making decisions more easily. Most recommender systems are based on two major approaches, which are Collaborative Filtering (CF) and Content-Based Filtering (CBF). Unlike CBF which pays attention to item attributes and individual interactions of a user to create an item, CF examines user behaviors and preferences of a population to determine the relevant items. The hybrid models unify these two methods in an attempt to ease their disadvantages, which include cold-start, sparsity, and lack of diversity. In addition to the development of algorithms, combination of deep learning and diversification methods has also contributed to the relevance, novelty, and adaptability of recommendations. This review explores the application of recommender system in retail with an overview of the principles of CF and CBF, their contribution to customer engagement, and the latest trends in hybrid and AI-driven recommendation systems. This paper synthesizes the main body of research to show the potential transformational nature of personalized recommendations in maximizing customer satisfaction, loyalty, and revenue generation and outlining the challenges and future research opportunities to further enhance the use of personalized recommendations in retail.
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