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Content and Context-Aware Recommender Systems for Business
Abstract
E-commerce activities among prominent retailing firms in modern times is inconceivable without the ubiquitous presence of recommender systems. This article brings forth the more advanced topics like content based and context-aware methods. Content-based methods use the actions and ratings of the users to match the user to new items based on past ratings. The objective here is to create user profiles and subsequently subject the profiles to classification algorithms. Knowledge-based systems are for more customized products with little history of usage and therefore little past data to help in recommendations. Such systems rely on either case-based recommendations or on a set of relevant constraints to identify appropriate recommendations. And finally, ensemble recommender systems help in combining the prediction power from multiple data sources. Finally, the author presents a discussion on the evaluation methods for recommender systems. The article is aimed towards both academic and managerial audiences.
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