Enhancement in Context-Aware Recommender Systems – A Systematic Review

A Recommendation System is a crucial Machine Learning algorithm that provides suggestions for various applications and uses computer algorithms or software to analyse and predict user preferences between products, content, services, or information. Within the realm, Recommendation Systems have become vital tools, utilizing Machine Learning algorithms to provide valuable suggestions across various applications. However, traditional Recommendation System encounters challenges related to scalability, sparsity, cold start issues, lack of diversity, and context-based recommendation, which results in user dissatisfaction and limited engagement. Addressing these issues, this study embarks on a comprehensive survey to propose a Context-Aware Recommendation System that integrates contextual factors such as the user's geographical position, current timing, emotional state, company, social engagements, psychological well-being, atmospheric conditions, direction, life stage, sensory information, gender, and personal profile. The survey meticulously investigates the potential enhancement of the Context-Aware Recommendation System by applying Deep Learning methodologies. The survey critically assesses and explores how advanced Deep Learning techniques can synergize with Context-Aware Recommendation System technology, overcome the limitations of current contextual systems, and ultimately optimize the recommendation process for an enriched user experience and enhanced system efficiency.