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Srinivas Gutta

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AAAI Conference 2000 Conference Paper

TV Content Recommender System

  • Srinivas Gutta
  • KP Lee
  • John Milanski
  • and John Zimmerman

The plethora of content available to the consumer has become overwhelming. Increasing amounts of information are being disseminated through terrestrial broadcast, satellite, and cable leading to an information overload. Common modes of searching for TV programs currently in existence include: TV-guide, PreVue channel and rudimentary search tools available through satellite dish TV programming service. These tools are general-purpose in nature and are not specifically tailored to the individual viewer’s taste. Towards that end we advance in this paper a recommender system that searches for TV programs based on their likes/dislikes through implicit personalization techniques.

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IJCAI Conference 1999 Conference Paper

Reinforcement Algorithms Using Functional Approximation for Generalization and their Application to Cart Centering and Fractal Compression

  • Clifford Claussen
  • Srinivas Gutta
  • Harry Wechsler

We address the conflict between identification and control or alternatively, the conflict between exploration and exploitation, within the framework of reinforcement learning. Qlearning has recently become a popular offpolicy reinforcement learning method. The conflict between exploration and exploitation slows down Q-learning algorithms; their performance does not scale up and degrades rapidly as the number of states and actions increases. One reason for this slowness is that exploration lacks the ability to extrapolate and interpolate from learning and to a large extent has to "reinvent the wheel". Moreover, not all reinforcement problems one encounters are finite state and action systems. Our approach to solving continuous state and action problems is to approximate the continuous state and action spaces with finite sets of states and actions and then to apply a finite state and action learning method. This approach provides the means for solving continuous state and action problems but does not yet address the performance problem associated with scaling up states and actions. We address the scaling problem using functional approximation methods. Towards that end, this paper introduces two new reinforcement algorithms, QLVQ and Quad-Q-learning, respectively, and shows their successful application for cart centering and fractal compression.

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