Relational Data Mining
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
Towards Automatic Generation of Query Taxonomy: A Hierarchical Query Clustering Approach
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Generating hierarchical summaries for web searches
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Auto-generation of topic hierarchies for web images from users' perspectives
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Proceedings of the 13th international conference on World Wide Web
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
IEEE Transactions on Knowledge and Data Engineering
LinkClus: efficient clustering via heterogeneous semantic links
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Generating semantically enriched user profiles for Web personalization
ACM Transactions on Internet Technology (TOIT)
Automatic taxonomy generation: issues and possibilities
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Data mining for web personalization
The adaptive web
Intelligent techniques for web personalization
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Preference elicitation techniques for group recommender systems
Information Sciences: an International Journal
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The effectiveness of incorporating domain knowledge into recommender systems to address their sparseness problem and improve their prediction accuracy has been discussed in many research works. However, this technique is usually restrained in practice because of its high computational expense. Although cluster analysis can alleviate the computational complexity of the recommendation procedure, it is not satisfactory in preserving pair-wise item similarities, which would severely impair the recommendation quality. In this paper, we propose an efficient approach based on the technique of Automated Taxonomy Generation to exploit relational domain knowledge in recommender systems so as to achieve high system scalability and prediction accuracy. Based on the domain knowledge, a hierarchical data model is synthesized in an offline phase to preserve the original pairwise item similarities. The model is then used by online recommender systems to facilitate the similarity calculation and keep their recommendation quality comparable to those systems by means of real-time exploiting domain knowledge. Experiments were conducted upon real datasets to evaluate our approach.