Fab: content-based, collaborative recommendation
Communications of the ACM
Learning user interest dynamics with a three-descriptor representation
Journal of the American Society for Information Science and Technology
Helping Online Customers Decide through Web Personalization
IEEE Intelligent Systems
Web page clustering using a self-organizing map of user navigation patterns
Decision Support Systems - Special issue: Web data mining
Adaptive User Modeling for Filtering Electronic News
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 4 - Volume 4
Self-Adaptive User Profiles for Large-Scale Data Delivery
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Multiple-vector user profiles in support of knowledge sharing
Information Sciences: an International Journal
A hybrid recommendation technique based on product category attributes
Expert Systems with Applications: An International Journal
Research of fast SOM clustering for text information
Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Collaborative user modeling for enhanced content filtering in recommender systems
Decision Support Systems
Hi-index | 12.06 |
User interest profile is the crucial component of most personalized recommender systems. The diversity and time-dependent evolving nature of user interests are creating difficulties in constructing and maintaining a sound user profile. This paper presents a simple but effective model, by using improved growing cell structures (GCS), to address this problem. The GCS is a kind of self-organizing map neural network with changeable network structure. By virtue of the clustering and structure adaptation capability of GCS, the proposed model maps the problem of learning and keeping track of user interests into a clustering and cluster-maintaining problem. Each cluster found by GCS represents an interest category of a user and the cluster maintaining, including cluster addition and deletion, corresponds to the addition of user's new interests and the removal of user's old interests. The proposed model has been validated by a set of experiments performed on a benchmark dataset. Results from experiments show that our model provides reasonable performance and high adaptability for learning user multiple interests and their changes.