Open Access Open Access  Restricted Access Subscription or Fee Access

A New Framework for Online Recommendation Systems Based on Open Approach Feedback


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecap.v9i5.16266

Abstract


The vastly growing amount of information spread on the internet on a daily basis has created a challenging process of decision making to the users in many aspects. The available options of everything requires an active procedure of filtering and prioritizing to make it easier for the users to make the right choices. The idea of Recommender systems is that it studies the behavior of users and their preferences and provide them with the most suitable option available based on personalized generation. The proposed framework discusses generating an interactive experience between the users and some inanimate websites by analyzing their buying behavior and their characteristics and takes away the effort needed from the users. Feedbacker is a proposed solution for providing the users with recommendations that best suit their preferences and needs. This research has achieved promising results comparing with other applications in terms of its impact on service quality and its impact on customer behavior. Based on the explicit user input regarding their interests in the Feedbacker service and based on the collected data in studying customers’ behaviors, it has been found that the system serves as a compass for researches and practices in the field of recommendation systems. It has approximately achieved 80% of the feedback process success and approximately 60% in terms of CRM success.
Copyright © 2019 Praise Worthy Prize - All rights reserved.

Keywords


Recommender System; Feedbacker; Social Network; Media; E-Business; CRM

Full Text:

PDF


References


F.O. Isinkaye ,Y.O. Folajimi, B.A. Ojokoh: Egyptian Informatics Journal. Recommendation systems: Principles, methods and evaluation Received 13 March 2015; revised 8 June 2015; accepted 30 June 2015.
https://doi.org/10.1016/j.eij.2015.06.005

Resnick P, Varian HR. Recommender system’s. Commun ACM 1997;40(3):56–8.

http://www.elsevier.com/locate/comphumbeh. Access: January 10th, 2018

http://www.electronics-tutorials.ws/systems/closed-loop-system.html. Access: April 30th, 2018

M. Gan, R. Jiang, Flower: Fusing global and local associations towards personalized social recommendation, Future Gener. Comput. Syst. 78 (2018) 462–473.
https://doi.org/10.1016/j.future.2017.02.027

Adomavicius G., Kwon Y., New Recommendation Techniques for Multi-Criteria Rating Systems, IEEE Intelligent Systems, 22(3), 48-55, May/June 2007.
https://doi.org/10.1109/mis.2007.58

R. Burke, Hybrid recommender systems: survey and experiments, User Model. User-Adap. Interact. 12 (4) ,2002 331–370.

N. Good , J. Schafer , J. Konstan , A. Borchers , B. Sarwar , J. Herlocker , J. Riedl , Combining collaborative filtering with personal agents for better recommen- dations, in: Proc. 16th Nat. Conf. Artif. Intell., 11th Innov. Appl. of Artif. Intell. Conf. (AAAI ’99/IAAI ’99), Orlando, USA, 1999, pp. 439–446.

Z. Abbassi , S. Amer-Yahia , L. Lakshmanan , S. Vassilvitskii , C. Yu , Getting recommender systems to think outside the box, in: Proc. RecSys 2009, 3rd ACM Conf. Recomm. Syst., 2009, pp. 285–288.
https://doi.org/10.1145/1639714.1639769

Y. Koren, R. Bell, C. Volinsky, Matrix factorization techniques for recommender systems, IEEE Computer Society, Volume 42 (Issue 8), 2009, Pages 30-37.
https://doi.org/10.1109/mc.2009.263

H. Ma, D. Zhou, C. Liu, M.R. Lyu, I. King, Recommender systems with social regularization, in: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, ACM, 2011, pp. 287–296.
https://doi.org/10.1145/1935826.1935877

H. Ma, I. King, M.R. Lyu, Learning to recommend with explicit and implicit social relations, ACM Trans. Intell. Syst. Technol. 2 (3) 2011, 29.
https://doi.org/10.1145/1961189.1961201

D.F. Gurini, F. Gasparetti, A. Micarelli, G. Sansonetti, Temporal people-topeople recommendation on social networks with sentiment-based matrix factorization, Future Gener. Comput. Syst. 78 (2018) 430–439.
https://doi.org/10.1016/j.future.2017.03.020

eprints.lse.ac.uk/12761/1/Multi-criteria_Analysis.pdf by JS Dodgson - ‎2009. Multi-criteria analysis: a manual 3. Contents.

Y. Qian, Y. Zhang, X. Ma, H. Yu, L. Peng, EARS: Emotion-aware Recommender System Based on Hybrid Information Fusion, Information Fusion, Volume 46, June 2018, Pages 141-146.
https://doi.org/10.1016/j.inffus.2018.06.004

C. Binucci, F. De Luca, E. Di Giacomo , G. Liotta, F. Montecchiani, Designing the Content Analyzer of a Travel Recommender System, Expert Systems with Applications, Volume 87, June 2017, Pages 199-208.
https://doi.org/10.1016/j.eswa.2017.06.028

A. Liu , S. Lu , Z. Zhang , T. Li , Y. Xie, Function Recommender System for Product Planning and Design, CIRP Annals- Manufacturing Technology, Volume 66(Issue 1),April 2017, Pages 181-184.
https://doi.org/10.1016/j.cirp.2017.04.041

M. Eirinaki , J. Gao , I. Varlamis , K. Tserpes, Recommender Systems for Large-Scale Social Networks: A review of challenges and solutions, Future Generation Computer System, Volume 78, September 2017, Pages 413-418.
https://doi.org/10.1016/j.future.2017.09.015

Mandave, D., Pole, G., SyntcRec: a Syntactic Recommender System Based on Improved Feature Selection Technique in Large Scholarly Data, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (6), pp. 537-544.
https://doi.org/10.15866/irecap.v7i6.13353

Ed Peleen, Rob Beltman, Customer Relationship Management (Edinburg Gate: Pearson Education limited., 2013) 2nd edition.

Feng Bai, Yafeng Qin, The Implementation of Relationship Marketing and CRM;JUN 2016. Journal of Business & Economic, Volume 3(Issue 2) June 2016, Pages 112-124.

Wei Wei, Ying (Tracy) Lu , Li Miao, Liping A. Cai, Chen-ya Wang, Customer-customer interactions (CCIs) at conferences: An identity approach, Tourism Management 59 (2017) 154-170.
https://doi.org/10.1016/j.tourman.2016.08.002

M. T. Ballestar, P. Grau-Carles, J. Sainz. Customer segmentation in e-commerce: Applications to the cashback business model, Journal Of Business Research, Volume 88, November 2017, Pages 407-414.
https://doi.org/10.1016/j.jbusres.2017.11.047


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize