2011 | OriginalPaper | Buchkapitel
Active Learning in Recommender Systems
verfasst von : Neil Rubens, Dain Kaplan, Masashi Sugiyama
Erschienen in: Recommender Systems Handbook
Verlag: Springer US
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Recommender Systems (RSs) are often assumed to present items to users for one reason – to
recommend
items a user will likely be interested in. Of course RSs do recommend, but this assumption is biased, with no help of the title, towards the “recommending” the system will do. There is another reason for presenting an item to the user: to learn more about his/her preferences, or his/her
likes
and
dislikes
. This is where Active Learning (AL) comes in. Augmenting RSs with AL helps the user become more self-aware of their own likes/dislikes while at the same time providing new information to the system that it can analyze for subsequent recommendations. In essence, applying AL to RSs allows for personalization of the recommending process, a concept that makes sense as recommending is inherently geared towards personalization. This is accomplished by letting the system actively influence which items the user is exposed to (e.g. the items displayed to the user during sign-up orduring regular use), and letting the user explore his/her interests freely.