International Journal of Information Engineering and Electronic Business(IJIEEB)

ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)

Published By: MECS Press

IJIEEB Vol.10, No.4, Jul. 2018

Survey on Personalized Web Recommender System

Full Text (PDF, 566KB), PP.33-40

Views:59   Downloads:0


Santosh Kumar, Varsha

Index Terms

Recommendation System;Navigation Behavior;demographic information


Recommendation system plays an essential role in searching any information from World Wide Web. Recommender system handles Information straining problem and improve customer correlation by providing best services. It suggests items or services to users according to their interest, navigation behavior or demographic information. This paper performs a survey on different approaches available for recommender system and performs a comparative analysis of different algorithms. Further, a discussion about various application areas has been done. At the end, issues and challenges in recommender systems have been discussed.

Cite This Paper

Santosh Kumar, Varsha," Survey on Personalized Web Recommender System", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.10, No.4, pp. 33-40, 2018. DOI: 10.5815/ijieeb.2018.04.05


[1]J.S.Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proc. 14th Conf, Uncertainty in Artificial Intelligence (UAI ’98), pp. 43-52, 1998.

[2]Atisha Sachan, “A Survey on Recommender Systems based on Collaborative Filtering Technique”‖, International journal of Innovations in Engineering and technology (IJIET), ISSN. 2319-1058, Volume 2 Issue 2, April 2013.

[3]Urmela, Dr. K. Suresh Joseph, K. Vaitheki “A Survey on Web Service Mining by Collaborative Filtering and QoS” International Journal of Recent Development in Engineering and Technology (IJRDET), ISSN 2347-6435 Volume 3, Issue 3, September 2014.

[4]I. H. and Frank I. Data Mining, Morgan Kaufman Publishers, San Francisco, 2000

[5]A.Elgohary, H. Nomir, I. Sabek, M. Samir, M. Badawy, and N.A.Yousri, “Wiki-rec: A semantic-based recommendation system usingwikipedia as an ontology,” in Intelligent Systems Design and Applications(ISDA), 2010 10th International Conference on,2010.

[6]K.O. et al.“Context-aware svstem for context-dependent information recommendation”.In International Conference On Mobile Data Management, 2006.

[7]Tejal Arekar, R.S. Sonar, Dr. N. J. Uke “A Survey on Recommendation System” International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163Volume 2 Issue 1, January 2015.

[8]Liu, J., Tang, M., Zheng, Z., et al.: Location-aware and personalized collaborative filtering for web service recommendation. IEEE Trans. Serv. Comput. 9(5), 686–699 (2016)

[9]X. Chen, Z. Zheng, X. Liu, Z. Huang, and H. Sun, ‘‘Personalized QoS-Aware Web Service Recommendation and Visualization,’’ IEEE Trans. Serv. Computing., vol. 6, no. 1, pp. 35-47, 2013.

[10]Zhang J, Peng Q, Sun S, Liu C. Collaborative filtering recommendation algorithm based on user preference derived from item domain features. Physica A: Statistical Mechanics and its Applications. 2014 Feb 15;396:66-76.

[11]Feng H, Tian J, Wang HJ, Li M. Personalized recommendations based on time-weighted overlapping community detection. Information & Management. 2015 Nov 30;52(7):789-800.

[12]Shen J, Deng C, Gao X. Attraction recommendation: Towards personalized tourism via collective intelligence. Neurocomputing. 2016 Jan 15;173:789-98.

[13]Mould DR. “Why therapeutic drug monitoring is needed for monoclonal antibodies and how do we implement this?” Clin Pharmacol Ther. 2016;99(4):351–4. doi: 10.1002/cpt.278

[14]Liu, Ning-Han. "Comparison of content-based music recommendation using different distance estimation methods." Applied intelligence 38.2 (2013): 160-174.

[15]Polatidis, Nikolaos, and Christos K. Georgiadis. "A multi-level collaborative filtering method that improves recommendations." Expert Systems with Applications 48 (2016): 100-110.

[16]Kang, Guosheng, et al. "AWSR: Active web service recommendation based on usage history." Web Services (ICWS), 2012 IEEE 19th International Conference on. IEEE, 2012.

[17]Xu, Wei, et al. "A personalized information recommendation system for R&D project opportunity finding in big data contexts." Journal of Network and Computer Applications 59 (2016): 362-369. 

[18]Shahabi, C., and Yi-Shin C., "An adaptive recommendation system without explicit acquisition of user relevance feedback." Distributed and Parallel Databases 14.2 (2003): 173-192. 

[19]Naruchitparames, Jeff, Mehmet Hadi Gunes, and Sushil J. Louis. "Friend recommendations in social networks using genetic algorithms and network topology." Evolutionary Computation (CEC), 2011 IEEE Congress on. IEEE, 2011.

[20]G. Adomavicius, A. Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions, IEEE Trans. Knowl. Data Eng. 17 (6), 2005, pp. 734–749.

[21]W. Lo, J. Yin, S. Deng, Y. Li, and Z. Wu, “An extended matrix factorization approach for qos prediction in service selection,” in Proc. of the 9th IEEE International Conference on Services Computing (SCC), , pp. 162, 2012.

[22]Lopes, Prajyoti, and Bidisha Roy. "Dynamic Recommendation System Using Web Usage Mining for E-commerce Users." Procedia Computer Science 45 (2015): 60-69.

[23]Recommender System Application Developments: A Survey Jie Lu, Dianshuang Wu, Mingsong Mao, Wei Wang, Guangquan Zhang

[24]R. Burke, Hybrid web recommender systems, in: P. Brusilovsky, A. Kobsa, W. Nejdl (Eds.) The Adaptive Web, Springer-Verlag, Berlin Heidelberg 2007, pp. 377-408.

[25]B. Smyth, Case-based recommendation, in: P. Brusilovsky, A. Kobsa, W. Nejdl (Eds.) The Adaptive Web, Springer Berlin Heidelberg2007, pp. 342-376.

[26]Yang, Xiwang, et al. "A survey of collaborative filtering based social recommender systems." Computer Communications 41 (2014): 1-10.

[27]Wang, Yuanyuan, Stephen Chi-Fai Chan, and Grace Ngai. "Applicability of demographic recommender system to tourist attractions: A case study on trip advisor." Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 03. IEEE Computer Society, 2012.

[28]M. Deshpande, G. Karypis, Item-based top-N recommendation algorithms, ACM Transactions on Information Systems (TOIS), 22 (2004) 143-177.

[29]M. Vozalis and K. G. Margaritis, “Collaborative filtering enhanced by demographic correlation,” in Proc. AIAI Symposium on Professional Practice in AI, of the 18th World Computer Congress, 2004.

[30]Naziha Abderrahim, Sidi Mohamed Benslimane, “Towards Improving Recommender System: A Social Trust-Aware Approach", IJMECS, vol.7, no.2, pp.8-15, 2015.DOI: 10.5815/ijmecs.2015.02.02 

[31]Asmaa Fridi, Sidi Mohamed Benslimane,"Towards Semantics-Aware Recommender System: A LOD-Based Approach", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.2, pp.55-61, 2017.DOI: 10.5815/ijmecs.2017.02.07

[32]Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inf. J. 16(3), 261–273 (2015)

[33]Sikka, R., Dhankhar, A., and Rana, C., "A Survey Paper on E-Learning Recommender System". International Journal of Computer Applications 47(9), 2012, pp. 27-30

[34]C. Chen, J. Zeng, X. Zheng, D. Chen, "Recommender system based on social trust relationships", Proc. IEEE 10th Int. Conf. e-Bus. Eng., pp. 32-37, 2013.