Solutions
In scenarios involving interface agents, the cold start problem may be overcome by introducing an element of collaboration amongst agents assisting various users. This way, novel situations may be handled by requesting other agents to share what they have already learnt from their respective users.
In recommender systems, the cold start problem is often reduced by adopting a hybrid approach between content-based matching and collaborative filtering. New items (which have not yet received any ratings from the community) would be assigned a rating automatically, based on the ratings assigned by the community to other similar items. Item similarity would be determined according to the items' content-based characteristics.
The construction of the user's profile may be automated by integrating information from other user activities, such as browsing histories. If, for example, a user has been reading information about a particular music artist from a media portal, then the associated recommender system would automatically propose that artist's releases when the user visits the music store.
Read more about this topic: Cold Start
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