Artyom Kravchenko. Evolution of Modern Recommender Systems with Ai Techniques


Natural Sciences / Computer Science / Artificial intelligence

Submitted on: Jun 25, 2019, 15:22:06

Description: Recommender systems (RSs) emerged several decades ago as software tools analyzing Web user preferences and making intelligent product and content suggestions based on their behavior and tastes. At first, they utilized simple comparison algorithms and analysis of explicitly stated user data in the form of ratings and likes. With the booming expansion of Web data and growing variety of products, services, and information sources online, RSs face pressing challenges to efficiency of recommendations such as data sparsity, cold start, and scalability requirements. The solutions to these issues come in the form of AI-based techniques E" advanced machine learning algorithms that adapt to the Web landscape and address the user requirements flexibly. With the trend of Web expansion certain to remain unchanged for many years to come, intelligent, AI-enriched RSs are expected to gain greater significance in usersE decision-making and Web navigation. But to gain user trust and ensure a greater user buy-in, RSs have to offer transparency and perceived credibility.

The Library of Congress (USA) reference page : http://lccn.loc.gov/cn2013300046.

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ai recommender.doc



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