Making Better Recommendations with Online Profiling Agents

Danny Oh, Chew Lim Tan

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. Even agent-based hybrid recommender systems that combine information filtering techniques with collaborative filtering techniques have been applied with considerable success only to simple consumer goods such as movies, books, clothing, and food. Yet complex, adaptive autonomous agent systems that can handle complex goods such as real estate, vacation plans, insurance, mutual funds, and mortgages have emerged. To a large extent, the reinforcement learning methods developed to aid agents in learning have been more successfully deployed in offline applications. The inherent limitations in these methods have rendered them somewhat ineffective in online applications. In this article, we postulate that a small amount of prior knowledge and human-provided input can dramatically speed up online learning. We 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.

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DOI: http://dx.doi.org/10.1609/aimag.v26i3.1823

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