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Title: Making better recommendations with online profiling agents
Keywords: Inference, electronic profiling, personalization, user preferences, reinforcement learning, interactive learning
Issue Date: 12-May-2004
Citation: OH CHIN HOCK, DANNY (2004-05-12). Making better recommendations with online profiling agents. ScholarBank@NUS Repository.
Abstract: In recent years, we have witnessed the success of autonomous agents applying machine learning techniques across a wide range of applications. However, agents applying the same machine learning techniques in online applications have not been so successful. Autonomous agent systems that can handle complex goods such as real estate, vacation plans, insurance, mutual funds, and mortgage have not yet emerged. To a large extent, the reinforcement learning methods employed have been more successfully deployed in offline applications. The inherent limitations in these methods have rendered them ineffective in online applications. In this thesis, we postulate that a small amount of prior knowledge and human-provided input can dramatically speed up online learning. From the experiments conducted, we were able to demonstrate that our agent HumanE - with its prior knowledge or "experiences" about the real estate domain - can effectively assist users in identifying requirements, especially unstated ones, quickly and unobtrusively.
Appears in Collections:Master's Theses (Open)

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