International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
Neurosymbolic AI for Explainable Recommendations in Frontend UI Design - Bridging the Gap between Data-Driven and Rule-Based Approaches Sunil Raj Thota1, Saransh Arora2 1Independent Researcher/Sr. Software Engineer, AI/ML & Full-stack Web Dev, Boston, MA, USA 2Independent Researcher/Sr. Data Engineer, AI/ML & Data Engineering, Seattle, WA, USA
-----------------------------------------------------------------------***--------------------------------------------------------------------Abstract- This paper proposes a novel approach utilizing neurosymbolic artificial intelligence (AI) techniques to enhance
the interpretability and effectiveness of recommendations in front-end user interface (UI) design. By integrating both datadriven and rule-based methodologies, our framework aims to bridge the gap between conventional recommendation systems and human-understandable decision-making processes. We leverage neurosymbolic AI to combine statistical learning from large-scale data with symbolic reasoning capabilities, enabling transparent and interpretable recommendations that align with user preferences and design principles. Through a series of experiments and case studies, we demonstrate the efficacy of our approach in providing explainable recommendations for front-end UI design tasks, facilitating more intuitive and user-centric interfaces.
Keywords-Neurosymbolic, AI, Data-Driven Approaches, Rule-Based Approaches Bridging the Gap Interpretability I INTRODUCTION In recent years, there has been growing interest in developing recommendation systems for frontend UI design that can effectively bridge the gap between data-driven approaches and rule-based methods. While data-driven techniques, such as collaborative filtering and matrix factorization, excel at capturing complex patterns in user preferences, they often lack transparency and interpretability. On the other hand, rule-based approaches offer explicit control over recommendation logic but may struggle to handle the vast amount of data and evolving user behaviors [1]. Researchers have resorted to neurosymbolic artificial intelligence, a novel technique that successfully blends the advantages of neural networks with symbolic reasoning, in order to address these issues. By integrating deep learning models with symbolic knowledge representations, neurosymbolic AI offers a promising framework for developing recommendation systems that are both accurate and interpretable. Furthermore, the incorporation of knowledge graphs provides a structured representation of domain-specific information, enabling more effective reasoning and decisionmaking. In this paper, we propose to explore the application of neurosymbolic AI for explainable recommendations in frontend UI design, with a particular focus on leveraging knowledge graphs. We aim to develop a recommendation framework that can seamlessly integrate data-driven insights with domain knowledge encoded in the form of a knowledge graph. In doing so, we aim to improve the transparency, interpretability, and effectiveness of recommendation systems for frontend UI design. In today's world, artificial intelligence (AI) has garnered widespread interest across a variety of application sectors, including the business sector. [2] Predictive maintenance, in particular, plays a significant role because it enables businesses to avoid internal system failures in a preventative manner and reduces the costs associated with business interruptions. People are now using techniques based on models and data to develop design, optimization, diagnostic, and maintenance stages. Model-based strategies utilize mathematical models, along with background information from human specialists, to achieve their goals. We utilize mathematical models to describe the interactions that govern a certain environment. On the other hand, data-driven approaches are inductive methods. These approaches involve the creation of models by generalizing from the data (that is, observations of the environment), with the objective of defining mathematical models based on the insights gained from the data. Since the models originate from the data, it is crucial to have a significant number of models that accurately reflect the region. The first method has problems with scalability and performance, while the second method is not interpretable and eliminates human engagement to some extent. Both methods have their drawbacks. Therefore, in order to make the most of the potential offered by both approaches while
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