Ganging up on information overload

When information is abundant, the knowledge of which information is useful and valuable matters most. We all use our network of family, friends, and colleagues to recommend movies, books, cars, and news articles. Collaborative filtering technology automates the process of sharing opinions on the relevance and duality of information. Collaborative filtering is one technique among many information filtering techniques that range from unfiltered to personalized and from effortless to laborious. Libraries or the Web are good examples of unfiltered information sources. E-mail directed to one recipient is a good example of a filtered information source. A best-seller list requires little effort fur the user, but provides the same recommendations to all users. Filters based on demographics, such as age, sex, or marital status, require some effort from the user in providing the demographics, and provide some level of personal filtering, so they are near the middle of the chart. Collaborative filtering requires relatively little effort from the user, and provides individually targeted recommendations, so it is in the upper right of the chart. Effort, of course, can be reduced via automation. While collaborative filtering is not necessarily effortless, it requires a relatively small amount of effort on the part of the user and provides very individualized recommendations. The collaborative filtering systems that we discuss here each offer a high degree of personalization, but each system takes a different approach to automation, attempting to find the best trade-off between the amount of work the users must put into the system and the perceived value and benefits they receive in return.