Generic placeholder image

Recent Patents on Engineering


ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

Research Article

A Hybrid Filtering Approach for an Improved Context-aware Recommender System

Author(s): Mugdha Sharma*, Laxmi Ahuja and Vinay Kumar

Volume 13 , Issue 1 , 2019

Page: [39 - 47] Pages: 9

DOI: 10.2174/1872212112666180813124358

Price: $65


Background: The domain of context-aware recommender approaches has made a substantial advancement over the last decade, but many applications still do not include contextual information while providing recommendations. Contextual information is crucial for various application areas and should not be ignored.

Objective: There are generally three algorithms which can be used to include context and those are - pre-filter approach, post-filter approach and contextual modeling. Each of the algorithms has their own drawbacks if any single approach is chosen. The goal of this work is to identify and propose a new hybrid approach which can include contextual information to improve the current movie recommender systems.

Method: Post evaluation of various patents related to recommender systems, the proposed approach modifies the post filter approach to rectify its shortcomings and combines it with the pre-filter approach based on the importance of contextual attribute provided by the user.

Results: The performance of the proposed system is measured in terms of precision of the system and ranking of the recommended movies to the user. The results of experimental setup also demonstrate that the proposed system improves the precision and ranking of the recommendations provided to the user.

Conclusion: With the help of this hybrid approach, the proposed system eliminates the problem of sparsity which is present in the pre-filter algorithm, and has performance improvement over the traditional post-filter approach. The proposed system will be vital for movie ticketing brands for the promotional purposes and various online content providers to recommend the accurate movies to their users.

Keywords: Context awareness, filtering algorithms, hybrid intelligent systems, post-filter, pre-filter, recommender systems.

Graphical Abstract
J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, "Recommender systems survey", Knowl. Base. Syst., vol. 46, pp. 109-132, 2013.
M.D. Ekstrand, J.T. Riedl, and J.A. Konstan, "“Collaborative Filtering Recommender Systems”, Found. Trends Human-Comput", Interact., vol. 04, pp. 81-173, 2011.
J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, "Recommender system application developments", Decis. Support Syst., vol. 74, pp. 12-32, 2015.
L. Chen, G. Chen, and F. Wang, "Recommender systems based on user reviews: the state of the art", User Model. User-adapt. Interact., vol. 25, pp. 99-154, 2015.
F.O. Isinkaye, Y.O. Folajimi, and B.A. Ojokoh, "Recommendation systems: principles, methods and evaluation", Egypt. J. Inf., vol. 16, pp. 261-273, 2015.
Y. Koren, Recommender system utilizing collaborative filtering combining explicit and implicit feedback with both neighborhood and latent factor models. U.S. Patent 8037080B2, 2008.
W.K. Hyung, H. Keejun, Y.Y. Mun, C. Joonmyun, and H. Jinwoo, "MovieMine: Personalized movie content search by utilizing user comments", IEEE Trans. Consum. Electron., vol. 58, pp. 1416-1424, 2012.
J. Zhang, Y. Lin, M. Lin, and J. Liu, "“An effective collaborative filtering algorithm based on user preference clustering”, Artif. Intell., Neural Networks Complex Probl.-", Solving Technol., vol. 45, pp. 230-240, 2016.
T. Chen, and L. He, "“Collaborative Filtering based on Demographic Attribute Vector”, In", ETP International Conference on Future Computer and Communication,. Wuhan, 2009, pp. 225- 229.
S.V. Aciar, G.I. Aciar, C.A. Collazos, and C.S. Gonzalez, "“User recommender system based on knowledge, availability, and reputation from interactions in foriums”,", Latin-Am. Learn. Technol.,. IEEE, vol. 11, pp. 18-22, 2016.
X. Cheng, D. He, and M. Fang, "“A hybrid collaborative filtering recommendation algorithm”, In", International Conference on Intelligent Information Processing,. Wuhan, 2016.
M. Nilashi, O. Ibrahim, and N. Ithnin, "Hybrid recommendation approaches for multi-criteria collaborative filtering", Expert Syst. Appl., vol. 41, pp. 3879-3900, 2014.
G. Anderson, and M. Schuster, “Content Recommendation System using a Neural Network Language Model”,. U.S. Patent 20150178265A1, 2013.
A.K. Dey, G.D. Abowd, and D. Salber, "A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications", Hum. Comput. Interact., vol. 16, pp. 97-166, 2001.
B.S. Sim, H. Kim, K.M. Kim, and H.Y. Youn, "“Type-based context-aware service recommender system for social network”, In", International Conference on Computer, Information and Telecommunication Systems,. Amman, 2012, pp. 1-5.
D. Mukherjee, S. Banerjee, S. Bhattacharya, and P. Misra, “Method and system for context-aware recommendation”,. U.S. Patent 20140123165A1, 2011.
G. Adomavicius, and A. Tuzhilin, "“Context aware recommendation systems”, In", ACM Conference on Recommender Systems,. Lausanne, 2008, pp. 335-336.
M.A. Domingues, and S.O. Rezende, "“The impact of contextaware recommender systems on music in the long tail”, In", Brazilian Conference on Intelligent Systems,. Fortaleza, 2013, pp. 119-124.
F. Ricci, L. Rokach, B. Shapira, and P.B. Kantor, Kantor, Recommendation Systems Handbook. New York, USA: Springer- Verlag, 2010.
U. Panniello, A. Tuzhilin, M. Gorgoglione, C. Palmisano, and A. Pedone, "“Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems”, In", Third ACM Conference on Recommender Systems,. New York, 2009, pp. 265-268.
"Ratings of 50 different movies. [online]", (Accessed on: 25 July 2017).
H. Wen, L. Fang, and L. Guan, "A hybrid approach for personalized recommendation of news on the Web", Expert Syst. Appl., vol. 39, pp. 5806-5814, 2012.
M. Gorgoglione, U. Panniello, and A. Tuzhilin, "“The effect of context-aware recommendations on customer purchaising behavior and trust”, In", Fifth ACM Conference on Recommender Systems,. Chicago, 2011, pp. 85-92.
Y. Hu, Y. Koren, and C. Volinsky, "“Collaborative filtering for Implicit Feedback Datasets”,", In Eighth International Conference on Data Mining,. Pisa, 2008, pp. 263-272.

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy