You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact

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


Publication Type:

Conference/Workshop Paper


6th European Conference on Case-Based Reasoning, ECCBR 2002




Collaborative filtering is often a 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 system’s 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.


author = {Mikael Sollenborn and Peter Funk},
title = {Category-Based Filtering and User Stereotype Cases to Reduce the Latency Problem in Recommender Systems},
pages = {395--405},
month = {September},
year = {2002},
booktitle = {6th European Conference on Case-Based Reasoning, ECCBR 2002},
publisher = {Springer},
url = {}