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Recommending suitable product items to the target user is essential for the continued success of eCommerce. Today, many E-commerce systems adopt numerous recommendation techniques. In this review paper, we analyse various hybrid recommender systems. Existing recommendation methods have challenges of data sparsity and efficiency, as the numbers of users, items, and interactions between the two in real-world applications increase fast. In this paper, we review relevant problems about a recommendation system and describe the relevant recommendation techniques used to overcome them. We also explore the evaluation process and proposed future research directions. We also identify newer challenges such as recommendations based on user profile and providing cross-domain recommendations. Network algorithm-based hybrid recommendation system is a good base with which we can respond best by discovering innovative options such as domain-specific recommendations, processing larger datasets, etc.
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