Recommending TV Programs: How far can we get at zero user effort?

RECOMMENDATION REQUIRES EFFORT Collaborative filtering methods have been applied to a number of domains like books, videos, audio CDs and Usenet news. These systems require some effort on the part of users before they can generate recommendations (see for example [5] on the “cold-start” problem). How much effort they require depends on domain and application. Some recommender systems for books or videos require rating a specific number of items before they generate the first prediction. If users can save money by not buying the wrong books and videos this effort might pay off. The Usenet news filter GroupLens [2] gathers ratings while users read articles so the process of building the profile and getting recommendations is interleaved. This strategy might lower the threshold for getting started. How much effort a user is willing to make depends not only on the domain but also on the personal value of the recommendation. Users who depend on the information in a newsgroup might make every required effort, but occasional users with a less serious information interest might not. To summarize: The higher the required effort the more potential users will quit.