LI-Tung Weng, Yue Xu, Yuefeng Li, Richi Nayak


Recommender systems have been widely applied in the domain of ecommerce. They have caught much research attention in recent years. They make recommendations to users by exploiting past users’ item preferences, thus eliminating the needs for users to form their queries explicitly. However, recommender systems’ performance can be easily affected when there are no sufficient item preferences data provided by previous users. This problem is commonly referred to as cold-start problem. This paper suggests another information source, item taxonomies, in addition to item preferences for assisting recommendation making. Item taxonomy information has been popularly applied in diverse ecommerce domains for product or content classification, and therefore can be easily obtained and adapted by recommender systems. In this paper, we investigate the implicit relations between users’ item preferences and taxonomic preferences, suggest and verify using information gain that users who share similar item preferences may also share similar taxonomic preferences. Under this assumption, a novel recommendation technique is proposed that combines the users’ item preferences and the additional taxonomic preferences together to make better quality recommendations as well as alleviate the cold-start problem. Empirical evaluations to this approach are conducted and the results show that the proposed technique outperforms other existing techniques in both recommendation quality and computation efficiency.


  1. Adomavicius, Gediminas, Ramesh Sankaranarayanan, Shahana Sen and Alexander Tuzhilin. 2005.
  2. s "Incorporating contextual information in recommender systems using a multidimensional approach " ACM Trans. Inf. Syst. 23(1):103-145.
  3. Breese, J. S., D. Heckerman and C. Kadie. 1998. "Empirical Analysis of Predictive Algorithms for Collaborative Filtering." In Proceedings of 14th Conference on Uncertainty in Artificial Intelligence. Madison, WI.
  4. Burke, Robin. 2002. "Hybrid Recommender Systems: Survey and Experiments." User Modeling and UserAdapted Interaction 12(4):331-370.
  5. Deshpande, Mukund and George Karypis. 2004. "Itembased top-N recommendation algorithms." ACM Transactions on Information Systems 22(1):143-177.
  6. Ferman, A. Mufit, James H. Errico, Peter van Beek and M. Ibrahim Sezan. 2002. "Content-based filtering and personalization using structured metadata." In 2nd ACM/IEEE-CS joint conference on Digital libraries Portland, Oregon, USA.
  7. Herlocker, Jonathan L., Joseph A. Konstan, Loren G. Terveen and John T. Riedl. 2004. "Evaluating collaborative filtering recommender systems." ACM Transactions on Information Systems (TOIS) 22(1):5- 53.
  8. Lemire, D. and A. Maclachlan. 2005. "Slope One Predictors for Online Rating-Based Collaborative Filtering." In 2005 SIAM Data Mining
  9. Middleton, Stuart E, Harith Alani, Nigel R Shadbolt and David C. De Roure. 2002. "Exploiting Synergy Between Ontologies and Recommender Systems." In The Semantic Web Workshop, World Wide Web Conference.
  10. Montaner, Miquel, Beatriz López and Josep Lluís De La Rosa. 2003. "A Taxonomy of Recommender Agents on the Internet." Artificial Intelligence Review 19(4):285-330.
  11. Park, Seung-Taek, David Pennock, Omid Madani, Nathan Good and Dennis DeCoste. 2006. "Naive filterbots for robust cold-start recommendations." In 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ed. ACM Press. Philadelphia, PA, USA.
  12. Sarwar, B., G. Karypis, J. Konstan and J. Riedl. 2000. "Application of dimensionality reduction in recommender systems--a case study." In ACM WebKDD Workshop. Boston, MA, USA.
  13. Schafer, J. Ben, Joseph A. Konstan and John Riedl. 2000. "E-Commerce Recommendation Applications." Journal of Data Mining and Knowledge Discovery 5:115-152.
  14. Schein, Andrew I., Alexandrin Popescul, Lyle H. Ungar and David M. Pennock. 2002. "Methods and metrics for cold-start recommendations" In 25th annual international ACM SIGIR conference on Research and development in information retrieval. Tampere, Finland: ACM Press.
  15. Ziegler, Cai-Nicolas, Georg Lausen and Lars SchmidtThieme. 2004. "Taxonomy-driven Computation of Product Recommendations" In International Conference on Information and Knowledge Management Washington D.C., USA

Paper Citation

in Harvard Style

Weng L., Xu Y., Li Y. and Nayak R. (2008). WEB INFORMATION RECOMMENDATION MAKING BASED ON ITEM TAXONOMY . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: ICEIS, ISBN 978-989-8111-39-5, pages 20-28. DOI: 10.5220/0001695100200028

in Bibtex Style

author={LI-Tung Weng and Yue Xu and Yuefeng Li and Richi Nayak},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: ICEIS,},

in EndNote Style

JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: ICEIS,
SN - 978-989-8111-39-5
AU - Weng L.
AU - Xu Y.
AU - Li Y.
AU - Nayak R.
PY - 2008
SP - 20
EP - 28
DO - 10.5220/0001695100200028