Ontology-based recommendation engines are a type of recommendation system that incorporates ontologies to enhance the recommendation process. An ontology is a formal representation of knowledge that defines the relationships between various concepts within a specific domain. In the context of recommendation engines, an ontology organizes and structures information about users, items, and their attributes, facilitating more intelligent and context-aware recommendations. KnowbasAI is an innovative Knowledge-Based AI System that revolutionizes custom recommendations in diverse domains. Leveraging a comprehensive ontology-driven knowledge base, KnowbasAI captures structured and unstructured data, including user interactions, product descriptions, and domain-specific knowledge. By assimilating this wealth of information, the system gains a profound understanding of user preferences and item attributes. KnowbasAI's recommendation engine uses a hybrid strategy that combines content-based filtering with collaborative filtering techniques. While content-based filtering aligns user preferences with pertinent items, collaborative filtering discovers user commonalities. Even for inexperienced or specialized users, the integration of various approaches guarantees precise and varied recommendations. KnowbasAI's clear and understandable suggestion approach is a key benefit. By using cutting-edge methods, the system offers insightful analyses of the decision-making process, boosting user confidence in the suggestions. With about 90% accuracy, it assists in achieving privacy and personalized recommendations for the users.
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