An Ontology Based Approach for User Preference Statistics
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Arguably the rapid development of Internet financial is one of the most significant breakthroughs in the financial domain. Automated financial statistics have gradually substituted the traditional manual statistical methods, providing a reliable data basis for economic planning. Therefore, the quality of a business activity heavily relies on the accuracy analysis of user preferences and recommend rated products to the users. Traditional item-based collaborative filtering method plays a dominant role for analyzing user preference and recommending the items for users, this method mainly utilize the fully rating data to predict whether the user like the target item. However, in many cases, the available user rating data is sparsely, which makes traditional item-based collaborative filtering method inefficient and inapplicable. To address this problem, this paper propose an ontology-based user preference statistical model (ontology-based UPS), where the concept and attribute features are extracted from financial ontology for semantic similarity computing; later, it is combined with the calculated rating similarities to improve the accuracy of the similar item set for the target item. The research results show that our approach outperformed traditional collaborative filtering method.
KeywordsCollaborative filtering Financial ontology User preference statistics Semantic information
This study is supported by the Beijing Natural Science Foundation of China with project no. Z160003.
- 3.Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of CSCW 1994. Chapel Hill, NC (1994)Google Scholar
- 4.Kluver, D., Ekstrand, M.D., Konstan, J.A.: Rating-based collaborative filtering: algorithms and evaluation In: Social Information Access, pp. 344–390 (2018)Google Scholar
- 6.Li, J., Liu, C., Liu, B., et al.: Diversity-aware retrieval of medical records. Comput. Ind. 69(1), 30–39 (2015)Google Scholar
- 7.An, Y., Hu, X., Song, I.Y.: Learning to discover complex mappings from web forms to ontologies. In: ACM International Conference on Information and Knowledge Management, ACM 2012, pp. 1253–1262 (2012)Google Scholar
- 8.Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS, pp. 3111–3119 (2013)Google Scholar
- 9.Zhao, J.C., Liu, S.H., Zhang, J.F.: Research on personalized recommendation system on item-based collaborative filtering algorithm. In: International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII), vol. 15, pp. 338–342 (2015)Google Scholar
- 11.Nguyen, H.V., Bai, L.: Cosine similarity metric learning for face verification. In: Asian Conference on Computer Vision, pp. 709–720 (2010)Google Scholar