Authors:
P. Aruna Saraswathy,M. Thangaraj,DOI NO:
https://doi.org/10.26782/jmcms.2020.06.00024Keywords:
Ontology,NLP,Recommender System,Knowledge Graph,Incremental Learning,Hybrid model,Semantic data model,Abstract
Recommender systems are implemented as information filtering agents. In most of the conventional recommender systems, the data about domain is available in limited volumes and suggestions are made to users based on their profile information. This lead to two major problems, insufficient representation of domain knowledge, called 'data sparsity' and lack of user-item interaction, called cold start. These two issues can be addressed with ontology based recommender systems, as they cam map domain information with user preferences without losing the semantic richness of the content. This work uses knowledge based method in knowledge aware recommendations to recommend most relevant research papers in digital literature collections. It uses simple methods to construct ontology knowledge graph and uses it for training incremental k-means clustering model. Finally, learning to rank, Adarank algorithm is used to list the top most recommendations for the given user query. The experiments were conducted based on real world unstructured datasets, and results have shown that the proposed model performs well over some of the state-of-the-art baselines.Refference:
I Agarwal, N., Haque, E., Liu, H., & Parsons, L. (2005, October). Research paper recommender systems: A subspace clustering approach. In International Conference on Web-Age Information Management. Springer, Berlin, Heidelberg. (pp. 475-491)
II Beel, J., Gipp, B., Langer, S., &Breitinger, C. (2016). paper recommender systems: a literature survey. International Journal on Digital Libraries, 17(4), 305-338.
III BessSchrader(2020). What’s the Difference Between an Ontology and a Knowledge Graph? Global Knowledge & Information Management Services. https://enterprise-knowledge.com/cms/assets/uploads/2020/01/Ontologies-vs.-Knowledge-Graphs.pdf.
IV Cao, Y., Wang, X., He, X., Hu, Z., & Chua, T. S. (2019, May). Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In The world wide web conference. (pp. 151-161).
V Cheyer, A. (2018). U.S. Patent No. 10,002,189. Washington, DC: U.S. Patent and Trademark Office.
VI Garanina, N., Sidorova, E., Kononenko, I., &Gorlatch, S. (2017). Using multiple semantic measures for coreference resolution in ontology population. International Journal of Computing, 16(3), 166-176.
VII Ge, J., Chen, Z., Peng, J., & Li, T. (2012, August). An ontology-based method for personalized recommendation. In 2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing (pp. 522-526). IEEE.
VIII George, G., &Lal, A. M. (2019). Review of ontology-based recommender systems in e-learning. Computers & Education, 142, 103642.
IX GiorgosPapachristoudis.(2019). Popular evaluation metrics in recommender systems explained. https:// medium.com/qloo/popular-evaluation-metrics-in-recommender-systems-explained-324ff2fb427d.
X Grossmann, R. (2019). The existence of the world: an introduction to ontology. Routledge. 06-Mar-Philosophy – 146 pages.
XI Iannacone, M., Bohn, S., Nakamura, G., Gerth, J., Huffer, K., Bridges, R., …&Goodall, J. (2015, April). Developing an ontology for cyber security knowledge graphs. In Proceedings of the 10th Annual Cyber and Information Security Research Conference (pp. 1-4).
XII Isinkaye, F. O., Folajimi, Y. O., &Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261-273.
XIII James Mishra. (2017).Mean Reciprocal Rank (MRR). https://machinelearning.wtf/terms/mean-reciprocal-rank-mrr/.
XIV Joachims, T., Granka, L., Pan, B., Hembrooke, H., & Gay, G. (2017, August). Accurately interpreting clickthrough data as implicit feedback. In ACM SIGIR Forum (Vol. 51, No. 1, pp. 4-11). New York, NY, USA: Acm.
XV Kawamura, T., Sekine, M., & Matsumura, K. (2017). Detecting Hypernym/Hyponym in Science and Technology Thesaurus Using Entropy-Based Clustering of Word Vectors. International Journal of Semantic Computing, 11(04), 433-449.
XVI Konys, A. (2018). Knowledge systematization for ontology learning methods. Procedia computer science, 126, 2194-2207.
XVII Li, G., & Chen, Q. (2016). Exploiting explicit and implicit feedback for personalized ranking. Mathematical Problems in Engineering.
XVIII Li, H. (2011). A short introduction to learning to rank. IEICE TRANSACTIONS on Information and Systems, 94(10), 1854-1862.
XIX Lv, F., Jin, T., Yu, C., Sun, F., Lin, Q., Yang, K., & Ng, W. (2019, November). SDM: Sequential deep matching model for online large-scale recommender system. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2635-2643).
XX Munir, K., &Anjum, M. S. (2018). The use of ontologies for effective knowledge modelling and information retrieval. Applied Computing and Informatics, 14(2), 116-126.
XXI Neethukrishnan, K. V., &Swaraj, K. P. (2017, February). Ontology based research paper recommendation using personal ontology similarity method. In 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1-4). IEEE.
XXII Obeid, C., Lahoud, I., El Khoury, H., &Champin, P. A. (2018, April). Ontology-based recommender system in higher education. In Companion Proceedings of the The Web Conference 2018 (pp. 1031-1034).
XXIII Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert systems with applications, 39(11), 10059-10072.
XXIV Parlak, B., &Uysal, A. K. (2019). On classification of abstracts obtained from medical journals. Journal of Information Science, 0165551519860982
XXV Pedregosa et al., (2011).Scikit-learn: Machine Learning in Python, JMLR 12, pp. 2825-2830.
XXVI Prokofyev, R., Tonon, A., Luggen, M., Vouilloz, L., Difallah, D. E., &Cudré-Mauroux, P. (2015, October). SANAPHOR: Ontology-based coreference resolution. In International Semantic Web Conference (pp. 458-473). Springer, Cham.
XXVII Raj.(2019). Creating Smart — Knowledge Base Systems (KBS) using advanced NLP library.Towards Data Science.May 4. https://towardsdatascience.com/creating-smart-knowledge-base-systems-kbs-using-advanced-nlp-library-b5c21dfafcd1.
XXVIII Sheridan, P., Onsjö, M., Becerra, C., Jimenez, S., &Dueñas, G. (2019). An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise. Future Internet, 11(9), 182.
XXIX Sure, Y., Tempich, C., &Vrandecic, D. (2006). Ontology engineering methodologies. Semantic Web Technologies: Trends and Research in Ontology‐based Systems, 171-190.
XXX Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., &Guo, M. (2019). Exploring high-order user preference on the knowledge graph for recommender systems. ACM Transactions on Information Systems (TOIS), 37(3), 1-26.
XXXI Weng, S. S., & Chang, H. L. (2008). Using ontology network analysis for research document recommendation. Expert Systems with Applications, 34(3), 1857-1869.
XXXII Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent trends in deep learning based natural language processing. ieee Computational intelligence magazine, 13(3), 55-75.
XXXIII Zhang, F., Yuan, N. J., Lian, D., Xie, X., & Ma, W. Y. (2016, August). Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 353-362).
XXXIV Thangaraj .M &ArunaSaraswathy.P. (2019, October). Ontology Based Recommender System using Fuzzy Clustering Technique. International journal of Engineering and Advanced Technology (IJEAT). 9(1). 6412-6418.