Computational Vision and Bio Inspired Computing pp 437-446 | Cite as
Personalized Research Paper Recommender System
Conference paper
First Online:
Abstract
Personalization is an emerging topic in the field of Research paper recommender systems and academic research. It is a technique to creative and efficient user profiles to achieve improved recommendations. Our work proposes a new user model to understand user behavior for personalization. This model initially extracts keywords based on the online behaviour of the user. The subsequent steps include concept extraction and user profile ontology construction to derive inferences and define relationships. The suggested model clearly depicts hierarchical ordering of the user’s long-term and current research interests. Furthermore, the adoption of our model contributes to improvement of recommendations.
Keywords
Personalization Ontology User profileReferences
- 1.Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRefGoogle Scholar
- 2.Seo, Y.-W., Zhang, B.-T.: A reinforcement learning agent for personalized information filtering. In: Proceedings of the 5th International Conference on Intelligent User Interfaces. ACM (2000)Google Scholar
- 3.Adomavicius, G., Tuzhilin, A.: Expert-Driven Validation of Rule-Based User Models in Personalization Application. Applications of Data Mining to Electronic Commerce, pp. 33–58. Springer, US (2001)zbMATHGoogle Scholar
- 4.Gauch, S., et al.: User profiles for personalized information access. Adapt Web 54–89 (2007) (Appendix: Springer-Author Discount)Google Scholar
- 5.Pazzani, M.J., Muramatsu, J., Billsus, D.: Syskill & Webert: Identifying interesting web sites. In: AAAI/IAAI, vol. 1 (1996)Google Scholar
- 6.Pretschner, A., Gauch, S.: Ontology based personalized search. In: Proceedingsof the 11th IEEE International Conference on Tools with Artificial Intelligence. IEEE (1999)Google Scholar
- 7.Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J ACM (JACM) 46(5), 604–632 (1999)Google Scholar
- 8.Trajkova, J., Gauch, S.: Improving ontology-based user profiles. Coupling approaches, coupling media and coupling languages for information retrieval. LE CENTRE DE HAUTES ETUDES INTERNATIONALES D’INFORMATIQUE DOCUMENTAIRE, 2004Google Scholar
- 9.Lieberman, H.: Letizia: an agent that assists web browsing. IJCAI (1) 1995, 924–929 (1995)Google Scholar
- 10.Chen, L., Sycara, K.: WebMate: A personal agent for browsing and searching. In: Proceedings of the Second International Conference on Autonomous Agents. ACM (1998)Google Scholar
- 11.Marais, H., Bharat, K.: Supporting cooperative and personal surfing with a desktop assistant. In: Proceedings of the 10th Annual ACM Symposium on User Interface Software and Technology. ACM (1997)Google Scholar
- 12.Adar, E., Karger, D., Stein, L.A.: Haystack: Per-user information environments. In: Proceedings of the Eighth International Conference on Information and Knowledge Management. ACM (1999)Google Scholar
- 13.Dumais, S., Cutrell, E., Cadiz, J.J., Jancke, G., Sarin, R., Robbins, D.C.: Stuff I’ve seen: A system for personal information retrieval and re-use. In: ACM SIGIR Forum, 49(2), 28–35. ACM (2016)Google Scholar
- 14.Mobasher, B.: Data Mining for Web Personalization. The Adaptive Web, pp. 90–135. Springer, Berlin, Heidelberg (2007)Google Scholar
- 15.Sieg, A., Mobasher, B., Burke, R.: Inferring user’s information context from user profiles and concept hierarchies. In: Classification, Clustering, and Data Mining Applications, pp. 563–573. Springer, Berlin, Heidelberg (2004)Google Scholar
- 16.Liu, F., Yu, C., Meng, W.: Personalized web search by mapping user queries to categories. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management. ACM (2002)Google Scholar
- 17.Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: IJCAI vol. 7, pp. 2670–2676 (2007)Google Scholar
- 18.Wu, F., Weld, D.S.: Open information extraction using Wikipedia. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics (2010)Google Scholar
- 19.Soderland, S., Roof, B., Qin, B., Xu, S., Etzioni, O.: Adapting open information extraction to domain-specific relations. AI Mag. 31(3), 93–102 (2010)Google Scholar
- 20.Venugopal, A., Ramesh, G.: A study on verbalization of OWL axioms using controlled natural language. Int. J. Appl. Eng. Res. (2015)Google Scholar
- 21.The 2012 ACM computing classification system. Retrieved November 22, 2017, from https://www.acm.org/publications/class-2012.
- 22.Gensim: models.tfidfmodel – TF-IDF model. Retrieved November 22, 2017, from https://radimrehurek.com/gensim/models/tfidfmodel.html
- 23.Sklearn.feature_extraction.text.TfidfVectorizer — scikit-learn 0.19.1 documentation. Retrieved November 22, 2017, from http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
- 24.Angeli, G., Premkumar, M.J., Manning, C.D.: Leveraging linguistic structure for open domain information extraction. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. ACL (2015)Google Scholar
- 25.Gowtham, R., Krishnamurthi, I.: PhishTackle—a web services architecture for anti-phishing. Cluster Comput. 17(3), 1051–1068 (2014)Google Scholar
- 26.Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
- 27.Dai, A.M., Olah, C., Le, Q.V.: Document embedding with paragraph vectors. arXiv preprint arXiv:1507.07998 (2015)
Copyright information
© Springer International Publishing AG 2018