An Adaptive Hybrid Recommender System Standard for Personalized Intelligent E-Business Ecosystems

This research aims at investigating how recommender systems standard can improve user experience and business performance in intelligent e-businesses. The study has examined the impact of recommender systems algorithms as well as their limitations. It attempts to identify important aspects which constitute the standard architecture of these systems within e-commerce environments. A mixed methods approach together with a combination of quantitative analysis of user behaviour data collected from an e-commerce platform and qualitative insights from user surveys or interviews has been employed by the study. Using this method, it aims to assess effectiveness of recommender systems and influence on purchase behaviour. The analysis of user behaviour data reveals patterns in user interactions with recommender systems, including click-through rates, conversion rates, and average order value. The qualitative insights obtained through user surveys and interviews provide valuable feedback on user satisfaction levels, perceived usefulness of recommendations, and areas for improvement in the recommender system design. The study concludes that recommender systems play a crucial role in driving user engagement and enhancing the overall user experience in e-commerce environment and there is no specific dedicated standard for the recommender system.

Recommendation Systems, E-commerce, Intelligent Systems, Standardization

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