Difference between revisions of "Cold-start problem"

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(one way of defining cold-start problems - add your definition!)
 
 
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One can distinguish two different types of cold-start scenarios:
 
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,
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* ''strict'' cold-start scenarios, where no collaborative information is available about the user/item in question, and
* and weak cold-start scenarios, where only some collaborative information is available about the user/item in question.
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* ''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,
 
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.
 
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.
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== External links ==
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* [[Wikipedia: Cold start]]
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[[Category: Task]]

Latest revision as of 08:57, 26 September 2011

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