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Web Content Recommendation Methods Based on Reinforcement Learning
Abstract
Information overload is no longer news; the explosive growth of the Internet has made this issue increasingly serious for Web users. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests. In this chapter we introduce our novel machine learning perspective toward the web recommendation problem, based on reinforcement learning. Our recommendation method makes use of the web usage and content data to learn a predictive model of users’ behavior on the web and exploits the learned model to make web page recommendations. Unlike other recommender systems, our system does not use the static patterns discovered from web usage data, instead it learns to make recommendations as the actions it performs in each situation. In the proposed method we combined the conceptual and usage information in order to gain a more general model of user behavior and improve the quality of web recommendations. A hybrid web recommendation method is proposed by making use of the conceptual relationships among web resources to derive a novel model of the problem, enriched with semantic knowledge about the usage behavior. The method is evaluated under different settings and it is shown how this method can improve the overall quality of recommendations.
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