View source for Cold-start problem

When past [[data]] is scarce, e.g. after the initial start of a [[recommender system]], or if a new [[user]] or [[item]] is added to the system, [[collaborative filtering]] methods are usually not able to come up with good predictions. Dealing with such situations is called the '''cold-start problem'''.

One can distinguish two different types of cold-start scenarios:
* ''strict'' cold-start scenarios, where no collaborative information is available about the user/item in question, and
* ''weak'' cold-start scenarios, where only some collaborative information is available about the user/item in question.

Typical ways of dealing with the cold-start problem are to use additional (non-collaborative) information about the new user/item,
e.g. [[content-based filtering]] (for new items), or trying to get the necessary preference information by interaction with user, e.g. using an [[active learning]] approach.

== External links ==
* [[Wikipedia: Cold start]]

[[Category: Task]]