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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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General Research Article

Improving Recommender Systems Using Co-Appearing and Semantically Correlated User Interests

Author(s): Bilal Hawashin*, Darah Aqel, Shadi Alzubi and Mohammad Elbes

Volume 13, Issue 2, 2020

Page: [240 - 247] Pages: 8

DOI: 10.2174/2213275912666190115162311

Price: $65

Abstract

Background: Recommender Systems use user interests to provide more accurate recommendations according to user actual interests and behavior.

Methods: This work aims at improving recommender systems by discovering hidden user interests from the existing interests. User interest expansion would contribute in improving the accuracy of recommender systems by finding more user interests using the given ones. Two methods are proposed to perform the expansion: Expanding interests using correlated interests’ extractor and Expanding interests using word embeddings.

Results: Experimental work shows that such expanding is efficient in terms of accuracy and execution time.

Conclusion: Therefore, expanding user interests proved to be a promising step in the improvement of the recommender systems performance.

Keywords: User interests, correlated interests, semantically correlated interests, word embeddings, recommender system, machine learning.

Graphical Abstract

[1]
"J.L. Herlocker J.L. Herlocker, J.A. Konstan,L.G. Terveen, and J.T. Riedl, “Evaluating collaborative filtering recommender systems", ACM Trans. Information Systems, vol. 22, no. 1, pp. 5-53. 2004
[2]
E. Rich, "User modeling via stereotypes", Cognit. Sci., vol. 3, no. 4, pp. 329-354, 1979.
[3]
D. Goldberg, D. Nichols, B.M. Oki, and D. Terry, "Using collaborative filtering to weave an information tapestry", Commun. ACM, vol. 35, no. 12, pp. 61-70, 1992.
[4]
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, "Group Lens: An open architecture for collaborative filtering of netnews", In: Proceedings of the 1994 ACM conference on Computer supported cooperative work.Chapel Hill, North Carolina, USA 1994, pp. 175-186.
[5]
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms", In: Proceedings of the 10th international conference on World Wide Web.Hong Kong, Hong Kong 2001, pp. 285-295
[6]
Y.H. Chien, and E.I. George, "A bayesian model for collaborative filtering", Proceedings for Seventh International Workshop in Artificial Intelligence and Statistics. 1999
[7]
D. Pavlov, and D. Pennock, A maximum entropy approach to collaborative filtering in dynamic, sparse, high-dimensional domains., Advan. Neur. Info. Process. Syst: Vancouver, Canada, 2002.
[8]
M.J. Pazzani, and D. Billsus, Content-based recommendation systems.The Adaptive Web.P. Brusilovsky, A. Kobsa, and W.Nejdl, eds.Springer-Verlag, . Vol. 4321, 2007, pp. 325-341
[9]
M. Pazzani, and D. Billsus, "Learning and revising user profiles: the identification of interesting web sites", Mach. Learn., vol. 27, pp. 313-331, 1997.
[10]
N. Littlestone, and M. Warmuth, "The weighted majority algorithm", Inf. Comput., vol. 108, no. 2, pp. 212-261, 1994.
[11]
R.J. Mooney, P.N. Bennett, and L. Roy, "Book recommending using text categorization with extracted information", In Proc.Recommender Systems Papers from 1998 Workshop, Technical Report WS-98-08,. 1998
[12]
S. Robertson, and S. Walker, "Threshold setting in adaptive filtering", J. Documentation., vol. 56, pp. 312-331, 2000.
[13]
I. Soboroff, and C. Nicholas, "Combining Content and Collaboration in Text Filtering", Proceedings of the IJCAI. Aug 1999
[14]
L.H. Ungar, and D.P. Foster, "“Clustering Methods for Collaborative Filtering”, AAAI workshop on recommendation systems", Technical Report. 1998, Vol. 1, pp. 114-129.
[15]
G. Adomavicius, and A. Tuzhilin, Context-Aware Recommender Systems.Recommender Systems Handbook: A Complete Guide for Research Scientists and Practitioners.L. Rokach, B. Shapira, P.Kantor, and F. Ricci, eds, Springer: Heidelberg, Berlin, 2011, pp. 217-250.
[16]
Y-K. Wang, "Context awareness and adaptation in mobile learning", In: The 2nd IEEE International Workshop on Wireless and Mobile Technologies in Education, 2004 Proceedings. 2004, pp.154-158.
[17]
A. Dey, G. Abowd, and D. Salber, "A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications", Human. Comput. Interact., vol. 16, pp. 97-166, Dec 2001.
[18]
B. Hawashin, A. Abusukhon, and A. Mansour, "An efficient user interest extractor for recommender systems", In: Proceedings of the World Congress on Engineering and Computer Science, vol. 2. 2015.
[19]
B. Hawashin, and A. Mansour, "An efficient agent based recommender system to extract interests of user groups", In: Proceedings of the World Congress on Engineering and Computer Science, vol. Vol. 1. 2016.
[20]
Y.Z. Wei, L. Moreau, and N.R. Jennings, "Learning users’ interests by quality classification in market-based recommender systems", IEEE Trans. Knowl. Data Eng., vol. 17, no. 12, pp. 1678-1688, December 2005.
[21]
A.M. Mansour, M.A. Obaidat, and B. Hawashin, "Elderly people health monitoring system using fuzzy rule based approach", Intl. J. Advan. Comp. Res., vol. 4, p. 904, 2014.
[22]
P. Vashisth, and P. Bedi, "Interest-based personalized recommender system", World Congress on Information and Communication Technologies. 2011
[23]
G. Aghili, M. Shajari, S. Khadivi, and M.A. Morid, "Using Genre Interest of Users to Detect Profile Injection Attacks in Movie Recommender Systems", Proc. 10th Ann. Conf. Machine Learning and ApplicationsIEEE, . 2011, pp. 245-250.
[24]
M.P.K. Reddy, and M.R. Babu, "A hybrid cluster head selection model for Internet of Things", Cluster Comput.. pp. 1-13,2017.
[http://dx.doi.org/10.1007/s10586-017-1261-1 2]
[25]
M.P.K. Reddy, and M.R. Babu, "Implementing self adaptiveness in whale optimization for cluster head section in internet of things", Cluster Comput.. 1-12, 2018.
[http://dx.doi.org/10.1007/s10586-017-1628-3 3]
[26]
M.P.K. Reddy, and M.R. Babu, "Energy efficient cluster head selection for internet of things", New Rev. Info. Network., vol. 22, pp. 54-70, 2017.
[27]
"Word2Vec Toolkit. Tool for computing continuous distributed representations of words", https://code.google.com/archive/p/word2vec/. Last Accessed: September 2018.
[28]
E. Frank, M.A. Hall, and I.H. Witten, The WEKA Workbench. Online Appendix for “Data mining: Practical machine learning tools and techniques”..Fourth Edition, Burlington: MA, Morgan Kaufmann,, 2016

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