International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 10, No. 10, 8 Oct. 2018
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Personalized Recommendation Systems (PRES): A Comprehensive Study and Research Issues
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Author(s)
Index Terms
Collaborative based filtering, content based filtering, hybrid filtering, personalized recommendation technique, recommendation system
Abstract
The type of information systems used to recommend items to the users are called Recommendation systems. The concept of recommendations was seen among cavemen, ants and other creatures too. Users often rely on opinion of their peers when looking for selecting something, this usual behavior of the humans, led to the development of recommendation systems. There exist various recommender systems for various areas. The existing recommendation systems use different approaches. The applications of recommendation systems are increasing with increased use of web based search for users’ specific requirements. Recommendation techniques are employed by general purpose websites such as google and yahoo based on browsing history and other information like user’s geographical locations, interests, behavior in the web, history of purchase and the way they entered the website.
Document recommendation systems recommend documents depending on the similar search done previously by other users. Clickstream data which provides information like user behavior and the path the users take are captured and given as input to document recommendation system. Movie recommendation systems and music recommendation systems are other areas in use and being researched to improve. Social recommendation is gaining the momentum because of huge volume of data generated and diverse requirements of the users. Current web usage trends are forcing companies to continuously research for best ways to provide the users with the suitable information as per the need depending on the search and preferences.
This paper throws light on common strategies being followed for building recommendation systems. The study compares existing techniques and highlights the opportunities available for research in this area.
Cite This Paper
Raghavendra C K, Srikantaiah K.C, Venugopal K. R, " Personalized Recommendation Systems (PRES): A Comprehensive Study and Research Issues", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.10, pp. 11-21, 2018. DOI:10.5815/ijmecs.2018.10.02
Reference
[1]Robin van Meteren and Maarten van Someren, “Using content based filtering for recommendation,” Conference Proceedings, semantics scholar, 2000.
[2]J. Bobadilla, F. Ortega, A. Hernando, A. Gutiérrez, “Recommender systems survey,” Knowledge-Based Systems, Vol. 46, pp.109–132, 2013.
[3]Billsus D, Pazzani MJ, “User modeling for adaptive news access,” User modeling and User-adapted Interaction, Vol. 10(2–3), pp.147–180, 2000.
[4]Raymond J. Mooney and Roy L, “Content-based book recommending using learning for text categorization,” Proceedings of the fifth ACM conference on digital libraries, ACM, pp. 195–204, 2000.
[5]Cacheda F, Carneiro V, Fern´andez D, Formoso V, “Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems,” ACM Transactions on the Web (TWEB), Article 2, Vol. 5(1), February 2011. “doi: 10.1145/1921591.1921593”.
[6]George A Sielis, Aimilia Tzanavari, George A Papadopoulos, “Recommender Systems Review: Types, Techniques and Applications,” In 3rd edition Encyclopedia of Information Science and Technology, pp.7260-7270, 2015.
[7]F.O. Isinkaye, Y.O. Folajimi, B.A. Ojokoh, “Recommendation systems: Principles, methods and Evaluation,” Egyptian Informatics Journal, Vol. 16(3), pp. 261-273, November 2015,
[8]Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D, Sartin M, “Combining content- based and collaborative filters in an online newspaper,” In Proceedings of ACM SIGIR workshop on recommender systems: algorithms and evaluation, Berkeley, California, 1999.
[9]Billsus D, Pazzani MJ, “A hybrid user model for news story classification,” In Proceedings of the seventh international conference on user modeling, Banff, Canada, Springer-Verlag, New York, pp. 99–108, 1999.
[10]Burke R, “Hybrid recommender systems: survey and experiments”, User modeling and User-adapted Interaction, vol. 12(4), pp.331–370, 2002. “doi:10.1023/A:1021240730564”.
[11]Smyth B, Cotter P, “A personalized TV listings service for the digital TV age,” Knowledge Based Systems, Vol. 13(2–3), pp.53–59, April 2000. ”https://doi.org/10.1016/S0950-7051(00)00046-0”
[12]Pazzani MJ, “A framework for collaborative, content-based and demographic filtering,” Artificial Intelligence Review, Vol. 13(5-6), pp. 393-408, December 1999.
[13]Basu C, Hirsh H, Cohen W, “Recommendation as classification: using social and content-based information in recommendation,” In Proceedings of the 15th national conference on artificial intelligence, Madison, WI, pp. 714–20, 1998.
[14]Yu Liu, Shuai Wang, M. Shahrukh Khan, and Jieyu He, “A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering,” Big Data Mining and Analytics, Vol. 1(3), pp. 211–221, September2018.”doi: 10.26599/BDMA.2018.9020019”
[15]F. S. Gohari, F. S. Aliee and H. Haghighi, “A trust-aware group recommender system using particle swarn optimization,” International Symposium on Computer Science and Software Engineering Conference (CSSE), Shiraz, pp. 80-85, 2017.
[16]B. K. Sunny, P. S. Janardhanan, A. B. Francis and R. Murali, “Implementation of a self-adaptive real time recommendation system using spark machine learning libraries,” IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), Kollam, pp. 1-7, 2017.
[17]Manasa.N, Bavya S, Kavitha G, “To Recommend Documents In Small Business Meetings by Extracting Keywords & Clustering them”, International Journal of Advance Research in Engineering, Science & Technology, Vol. 3(7), July-2016,
[18]Michael J. Pazzani and Daniel Billsus, “Content Based Recommendation System”, The Adaptive Web, Springer, LNCS 4321, pp. 325 – 341, 2007.
[19]RanaChamsi Abu Quba, “On Enhancing Recommender Systems by Utilizing general Social Networks Combined with User’s Goals and Contextual Awareness,” Networking and Internet Architecture, University Claude Bernard - Lyon I, 2015
[20]S. Meng, W. Dou, X. Zhang and J. Chen, “KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications,” IEEE Transactions on Parallel and Distributed Systems, Vol. 25(12), pp. 3221-3231, December 2014.
[21]Back Sun Sim, Heeseong Kim, KwangMyung Kim and H. Y. Youn, “Type-based context-aware service Recommender System for social network,” International Conference on Computer, Information and Telecommunication Systems (CITS), Amman, pp. 1-5, 2012.
[22]V. B. Savadekar and M. E. Patil, “Improved recommendation system with review analysis,” International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), Jalgaon, pp. 79-82, 2016.
[23]H. L. Nguyen and J. J. Jung, “Utilizing Dynamics Patterns of Trust for Recommendation System,” International Conference on Intelligent Environments (IE), Seoul, pp. 108-113, 2017.
[24]Zhang and Xiaoying, “Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations,” Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 98-106, 2017.
[25]EvangeliaChristakopoulou and George Karypis, “Local Item-Item Models for Top-N Recommendation,” Proceedings of the 10th ACM Conference on Recommender Systems, pp. 67-74, 2016.
[26]Xiaoming Liu, Chao Shen, “We know who you are: Discovering similar groups across multiple social networks,” accepted for inclusion in IEEE Transactions on Systems, Man and Cybernetics Systems, 2018.
[27]Surong Yan, Kwei-Jay Lin, Xiaolin Zheng and Xiaoqing Feng, “An approach for building efficient and accurate social recommender systems using individual relationship networks”, IEEE Transactions on Knowledge and Data Engineering, Vol. 29(10), October 2017.
[28]Xiwang Yang, Chao Liang, Miao Zhao, Hongwei Wang, Hao Ding, Yong Liu, Yang Li and Junlin Zhang, “Collaborative filtering based recommendation of online social voting”, IEEE Transactions on Computational Social Systems, Vol. 4(1), March 2017.
[29]Anitha Anandhan, Liyana shuib, Maizatul Akmar Ismail, and Ghulam Mujtaba, “Social Media Recommender Systems: Review and Open Research Issues,” In IEEE Access, April 2018.“doi: 10.1109/ACCESS.2018.2810062”
[30]Ningning Yi, Chunfang Li, Xin Feng, Minyong Shi, “Design and Implementation of Movie Recommender System Based on Graph Database,” 14th Web Information Systems and Applications Conference, 2017. “DOI 10.1109/WISA.2017.34”