Algorithms for clustering data
Algorithms for clustering data
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
A Case-Based Reasoning View of Automated Collaborative Filtering
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
WAP ing the Web: Content Personalisation for WAP-Enabled Devices
AH '00 Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Improving the Quality of the Personalized Electronic Program Guide
User Modeling and User-Adapted Interaction
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
An empirical testing of user stereotypes of information retrieval systems
Information Processing and Management: an International Journal - Special issue: Cross-language information retrieval
On exploiting classification taxonomies in recommender systems
AI Communications - Recommender Systems
Hierarchical-Hyperspherical Divisive Fuzzy C-Means (H2D-FCM) Clustering for Information Retrieval
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A knowledge-based product recommendation system for e-commerce
International Journal of Intelligent Information and Database Systems
A quality driven Hierarchical Data Divisive Soft Clustering for information retrieval
Knowledge-Based Systems
HYREC: a hybrid recommendation system for e-commerce
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
A flexible news filtering model exploiting a hierarchical fuzzy categorization
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
Supporting information spread in a social internetworking scenario
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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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 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.