Category-Based Filtering and User Stereotype Cases
to Reduce the Latency Problem in Recommender Systems

Mikael Sollenborn and Peter Funk

Mlardalen University
Department of Computer Science and Engineering
Vsters, Sweden
{mikael.sollenborn, peter.funk}@idt.mdh.se



Abstract. Collaborative filtering is an often successful method for personalized
item selection in Recommender systems. However, in domains where items are
frequently added, collaborative filtering encounters the latency problem.
Characterized by the systems inability to select recently added items, the
latency problem appears because new items in a collaborative filtering system
must be reviewed before they can be recommended. Content-based filtering
may help to counteract this problem, but runs the risk of only recommending
items almost identical to the ones the user has appreciated before. In this paper,
a combination of category-based filtering and user stereotype cases is proposed
as a novel approach to reduce the latency problem. Category-based filtering
puts emphasis on categories as meta-data to enable quicker personalization.
User stereotype cases, identified by clustering similar users, are utilized to
decrease response times and improve the accuracy of recommendations when
user information is incomplete.
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