Semantic Approach for Multifarious Ranked Novelty and Diversity in News Recommendations
DOI:
https://doi.org/10.51983/ajeat-2017.6.2.821Keywords:
User profile, OWL ontology,, recommender system, Semantic Expansion, Novelty, DiversityAbstract
Information overload on web creates lots of inconvenience for end users to unearth requisite information which instigate the demand of personalized recommendations. Recommender systems strive to attain just accuracy in recommendations based on the history of user preferences, resulting in over specialized recommendations. This leads to gradual loss of user’s interest in the system. These approaches also fail to recommend other products in long list which could be of user interest but user is not aware of. This in-turn leads to sparse user profiles. In dynamic domains, novelty and diversity in coverage can handle over specialization problem. To bring useful novelty and diversity, it is proposed to semantically expand the user preferences. For this purpose our designed multi level ontology is used with linkages among concepts, entities and properties, carrying different weights. Ontology is also enriched with different relations from online semantic lexicon and annotated with additional information about entities from open and linked external knowledge source. For relevant accurate recommendations in highly dynamic domains, we propose semantic approach to implicitly capture individual user preferences in separate profiles. One profile is to capture short term dynamic interests with temporal effects and another is to capture long term static interests. Semantic profiling helps to handle preferences in manageable number of news categories which are arranged hierarchically. It also helps to handle synonymy and polysemy problems, reducing ambiguity in profiling. An additional important step of outlier analysis and rectification is proposed to handle the effects of sudden unexpected temporary drifts in dynamic user preferences. This focused analysis helps to maintain correct ranking of user preferences which is generally overlooked. The rectified ranking of user interests is fetched for semantic expansion of user preferences, to bring correct ranking in diversity and novelty. Proposed approach semantically brings reasonable multifarious novelty and diversity by analyzing different features of item. Testing is based on live data gathered from RSS feeds of popular news providers for trustworthiness. Transparency is achieved by presenting new recommendations in separate sections along with explicit trace path options, based on difference in approaches. Transparency assists end user in making decision. User’s interests in diverse and novel recommendations are updated in their individual profiles, resulting in reduction of sparsity.
References
S. Agarwal, A. Singhal, and P. Bedi, "IPTC based ontological representation of educational news RSS feeds," in Proceedings of ITC 2012 – 3rd International Conference on Recent Trends in Information, Telecommunication and Computing, Aug 03-04, 2012, Bangalore, India, Lecture Notes in Electrical Engineering, vol. 150, pp. 353-359, 2013.
S. Agarwal, A. Singhal, and P. Bedi, "Classification of RSS feed news items using ontology," in Proceedings of ISDA 2012 - 12th International Conference on Intelligent System Design and Applications, Nov 27-29, 2012, Kochi, India, pp. 491-496. USA: IEEE Xplore, 2012.
S. Agarwal and A. Singhal, "Autonomous ontology population from DBpedia based on context sensitive entity recognition," in Fourth International Joint Conference on Advances in Engineering and Technology, AET 2013, Elsevier, 2013, pp. 580-589. ACEEE Conference Proceeding Series 02.
S. Agarwal and A. Singhal, "Handling skewed results in news recommendations by focused analysis of semantic user profiles," in International Conference on Reliability Optimization & Information Technology, ICROIT 2014, IEEE Delhi Section, Jan 01, 2014, pp. 74-79.
S. Agarwal, A. Singhal, P. Bedi, J. Ena, and G. Gupta, "Proactive predictions to handle issues in recommendations," in 4th IEEE International Advanced Computing Conference - IACC 2014, pp. 555-560.
A. Ahu, B. Mobasher, and R. Burke, "Web search personalization with ontological user profiles," in CIKM’07 Lisboa, Portugal ‘07, ACM, 2007, pp. 978-1-59593-803-9/07/0011.
A. Montes-Garcia, J. M. Marco, and J. E., "Towards a journalist based news recommender system: The Wesomender approach," International Journal: Expert Systems with Applications, vol. 40, pp. 6735-6741, 2013.
C. C. Chen, M. C. Chen, and Y. Sun, "PVA: A self-adaptive personal view agent system," in Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2008.
D. Goldberg, D. Nichols, B. Oki, and D. Terry, "Using collaborative filtering to weave an information tapestry," Communications of the Association of Computing Machinery, vol. 35, no. 12, pp. 61-70, 1992.
T. R. Gruber, "A translation approach to portable ontology specifications," Knowledge Acquisition, vol. 5, no. 2, pp. 199-220, 1993.
H. Wen, L. Fan, and L. Guan, "A hybrid approach for personalized recommendation of news on the web," International Journal: Expert Systems with Applications, vol. 39, pp. 5806-5814, 2012.
I. Lasek, "DC Proposal: Model for news filtering with named entities," in International Semantic Web Conference (2), 2011, pp. 309-316.
J. Liu, P. Dolan, and E. R. Pedersen, "Personalized news recommendations based on click behavior," in Proceedings of the 15th international conference on Intelligent user interfaces, ACM, 2010, pp. 31-40.
J. M. Alvarez, L. Polo, W. et al., "Application of the spreading activation technique for recommending concepts of well known ontologies in medical systems," in ACM-BCB Chicago, USA, 2011, pp. 978-1-4503-0796-3/11/08.
L. Li, D. Wang, T. Li, et al., "SCENE: A scalable two-stage personalized news recommendation system," in SIGIR’11, July 24–28, 2011, Beijing, China, Copyright 2011 ACM 978-1-4503-0757-4/11/07.
L. T. Liang, Y. F. Yang, D. N. Chen, and Y. C. Ku, "A semantic expansion approach to personalized knowledge recommendation," ScienceDirect Decision Support Systems, vol. 45, pp. 401-412, 2008.
Y. Lv, T. Moon, P. Kolari, Z. Zheng, X. Wang, and Y. Chang, "Learning to model relatedness for news recommendation," in Proceedings of the 20th international conference on World wide web, ACM, 2011, pp. 57–66.
P. Melville and V. Sindhwani, "Recommender systems," in Encyclopedia of Machine Learning, C. Sammut and G. Webb (Eds.), pp. 829-837, Springer, 2010.
M. Z. Ge, C. Dietmar, "Beyond accuracy: Evaluating recommender systems by coverage and serendipity," in RecSys Barcelona, Spain, ACM, 2010, pp. 978-1-60558-906-0/10/09.
R. Neches, R. Fikes, T. Finin, T. Gruber, R. Patil, T. Senator, and W. R. Swartout, "Enabling technology for knowledge sharing," AI Magazine, vol. 12, no. 3, pp. 36-56, 1991.
Q. Liu, E. Chen, and C. H. Q. Ding, "Enhancing collaborative filtering by user interest expansion via personalized ranking," in IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, vol. 42, no. 1, February 2012, pp. 1083-4419/$26.00 © 2011 IEEE.
F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor (Eds.), Recommender Systems Handbook, Springer, 2011.
S. R. Wolfe and Y. Zhang, "Interaction and personalization of criteria in recommender systems," in LNCS 6075, Springer-Verlag Berlin Heidelberg, 2010, pp. 183–194.
M. Uschold and M. Gruninger, "Ontologies: Principles, methods and applications," Knowledge Engineering Review, vol. 11, no. 2, pp. 93-136, 1996.
S. Vargas and P. Castells, "Rank and relevance in novelty and diversity metrics for recommender systems," in 8th ACM International Conference on Recommender Systems (RecSys 2011), Chicago, IL, 2011, pp. 109-116.
Z. Su, J. Yan, H. Ling, and H. Chen, "Research on personalized recommendation algorithm based on ontological user interest model," in Journal of Computational Information Systems, vol. 8, pp. 1169–1181, 2012.
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