Presentation 21 November 2012

Thesis presentation 1, 21 November 2012

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2 responses to “Presentation 21 November 2012

    • The cold start problem refers to a situation in which the system has either little information on a user (e.g. a new user with no rating history) or on certain items (typically when new items are introduced into a collaborative filtering system, there are few ratings for this item, so it will be hard to establish a good guess of what a random user might rate this item) or both.

      To solve this problem, one approach may be to refine your algorithm, but in the end if there is not enough data, there is just not enough data to draw any conclusions from. What usually happens is that the designers of such systems try to neutralize the effect of the cold start problem. Often this comes down to providing the user with some sort of insight into the recommender system and/or user interaction to steer the recommendation process; e.g. Pharos (Zhao, 2010).

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