Learning Classifier Systems as Recommender Systems

S. Babu, and S. von Mammen. Lifelike Computing Systems Workshop LIFELIKE 2022 , (2022)


A multitude of software domains, ranging from e-commerce services to personal digital assistants, rely on recommender systems (RS) to enhance their performance and customer experience. There are several established approaches to RS, including algorithms, namely, collaborative filtering, content based filtering, and various hybrid systems that use different verticals of data, such as the user’s preferences, the metadata of items to be suggested, etc., to generate recommendations. Contrary to those, we adopt a classifier system to benefit from its rule-based knowledge representation and interwoven learning mechanisms. In this paper, we present our approach and elaborate on its potential use to flexibly and effectively guide a user through the application of an authoring platform.

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