Connection Guidance For Cold Start Users
TRUST NETWORKS FOR RECOMMENDER SYSTEMS(2011)
摘要
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.
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