Connection Guidance for Cold Start Users

Abstract

In Chap. 5, we briefly discussed the most common limitations of recommender systems. In this chapter, we go more deeply into one of their main challenges, namely the user cold start problem. Due to lack of detailed user profiles and social preference data, recommenders often face extreme difficulties difficult to generate in generating adequately personalized recommendations for new users. Some systems therefore actively encourage users to rate more items. The interface of the online DVD rental service Netflix for example explicitly hides two movie recommendations, and promises to reveal these after the user rates his most recent rentals. Since it is very important for e-commerce applications to satisfy their new users (who might be on their way to become regular customers), it does not come as a surprise that the user cold start problem receives a lot of attention from the recommender system community.

Six degrees of separation doesn’t mean that everyone is linked to everyone else in just six steps. It means that a very small number of people are linked to everyone else in a few steps, and the rest of us are linked to the world through those special few. The Tipping Point, 2000. Malcolm Gladwell