Interactive Interface Agents as Recommender Systems

Michael Fleming and Robin Cohen

This paper presents a model for more interactive interface agents. Using learning interface agents is one strategy for designing recommender systems. The more interactive style of agents presented in this paper aims to increase the trust and understanding between the user and the agent, by allowing the agent to solicit further input from the user under certain conditions. We illustrate our design for more interactive interface agents by including some examples in the domain of electronic mail. We then discuss why the model is also applicable to designing recommender systems, in general.


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