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Double Agents

April 1, 2006 | Gerald HÌ_ubl, Kyle B. Murray | Data, AI, & Machine Learning
Electronic information can easily overwhelm people. Too much information often strains attention, memory, motivation or other factors. In response to this challenge, software tools have been introduced to assist humans in filtering and organizing information into more digestible amounts and formats. Many of these tools are altruistic in the sense that they have no vested interest in what the user does with the information. However, some are designed not only to assist buyers but also to steer them in a particular direction. This makes them "double agents." Examples of such decision aids exist at a number of vendor sites and include Amazon.com's "Your Store," GM's "Auto Choice Advisor" and IBM's "Solution Profiler." This article reviews the research on electronic recommendation agents. It focuses on tools that provide online shoppers with personalized product recommendations and explores the benefits and potential difficulties consumers may experience when using such tools. The article examines how electronic agents that provide personalized product recommendations affect the effort-accuracy trade-off in decision making and looks at the benefits recommendation agents can bring consumers. It concludes that having a consumer-centric double-agent perspective can lead to a deeper and more comprehensive understanding of the interaction between electronic product-recommendation agents and consumers. This, in turn, can spur the design of better and more effective recommendation tools that allow consumers to speed up their decision processes and improve the quality of the choices they make.
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