Developing Hybrid Intelligence Based Recommender System:
A Search Towards Machine Learning Components
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The scope of harnessing information science has been experienced in personalized decision support systems. This involves minimizing the volume of information and making deductions, such as in the case of Amazon's recommendation system. Traditional recommendation systems are becoming outdated and inadequate in meeting user requirements and technological trends. New recommendation systems like contextual, group, and social recommendation have been discovered. These systems have been investigated and analyzed using nature-inspired algorithms, evolutionary algorithms, swarm intelligence algorithms...
The scope of harnessing information science has been experienced in personalized decision support systems. This involves minimizing the volume of information and making deductions, such as in the case of Amazon's recommendation system. Traditional recommendation systems are becoming outdated and inadequate in meeting user requirements and technological trends. New recommendation systems like contextual, group, and social recommendation have been discovered. These systems have been investigated and analyzed using nature-inspired algorithms, evolutionary algorithms, swarm intelligence algorithms, and machine learning techniques to provide more precise personalized recommendations. A community-based filtering algorithm is proposed as well as an innovative hybrid intelligent algorithm to handle non-erroneous recommendations in a context-aware framework and address threats from intruders using optimization techniques and.The work aims to provide efficient solutions to problems faced by users, including sparsity, novelty, precise recommendation, and optimum decision-making solutions. The proposed models have been extensively experimented with and show superior learning mechanisms.
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