DEVELOPING INNOVATIVE RECOMMENDATION AND PERSONALIZATION ENGINES TO IMPROVE THE USER EXPERIENCE ON THE TRADING PLATFORM
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Abstract
This article focuses on the development of innovative recommendations and personalization mechanisms to enhance the user experience in a shopping platform. The objective is to provide users with customized and relevant product recommendations, thereby improving user engagement and satisfaction. Advanced data analytics techniques and machine learning algorithms are employed to analyze user preferences, historical purchase data, and contextual information. By leveraging this information, the recommendation engine generates personalized recommendations that align with each user's interests and preferences. Additionally, the article explores the implementation of dynamic personalization mechanisms, such as adaptive user interfaces and real-time updates, to create a seamless and intuitive shopping experience. The findings highlight the potential of these innovative approaches to significantly enhance user engagement, increase conversion rates, and foster long-term customer loyalty on shopping platforms.
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