A Hybrid Recommendation System Based on the Supply Chain in Social Networks
DOI:
https://doi.org/10.13052/jwe1540-9589.2133Keywords:
Recommendation System, Social Networks, Supply Chain, Artificial Neural Network, Fuzzy LogicAbstract
With the expansion of virtual social networks, finding and recommending appropriate and favorite information and items to users is one of the severe issues in their development. To this end, recommender systems predict and recommend interests based on past behavior reviews and user preferences. However, less research has been done on people to people in social networks, and it is still based on exploring communication and friendship circles, which is generally not desirable for specialized users. Social networks include a variety of entities such as individuals, businesses, companies, and technical communications that also contain a variety of information related to the supply chain interaction, such as industries, functions, and communications between them and users.
This paper provides a recommendation system framework for recommending people to people in social networks based on supply chain interactions. For this purpose, it has presented five hybrid methods based on artificial neural networks and fuzzy strategies to provide better and more accurate recommendations than basic methods. Eventually, a case study was conducted on the LinkedIn social network to show the improvements in applying this new approach to primary methods. In this regard, seven specific evaluation criteria of recommender systems have been used.
Downloads
References
Khoshnood F, Mahdavi M, Sarkaleh MK. Designing a recommender system based on social networks and location based services. International Journal of Managing Information Technology. 2012;4(4):41.
Natarajan S, Vairavasundaram S, Natarajan S, Gandomi AH. Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data. Expert Systems with Applications. 2020;149:113248.
Sinha BB, Dhanalakshmi R. Building a Fuzzy Logic-Based Artificial Neural Network to Uplift Recommendation Accuracy. The Computer Journal. 2020;63(11):1624–32.
Kunaver M, Požrl T. Diversity in recommender systems–A survey. Knowledge-Based Systems. 2017;123:154–62.
Damiani E, Ceravolo P, Frati F, Bellandi V, Maier R, Seeber I, et al. Applying recommender systems in collaboration environments. Computers in Human Behavior. 2015;51:1124–33.
Cai X, Hu Z, Zhao P, Zhang W, Chen J. A hybrid recommendation system with many-objective evolutionary algorithm. Expert Systems with Applications. 2020;159:113648.
Aggarwal CC. Recommender systems: Springer; 2016.
Hwangbo H, Kim YS, Cha KJ. Recommendation system development for fashion retail e-commerce. Electronic Commerce Research and Applications. 2018;28:94–101.
Sun Z, Han L, Huang W, Wang X, Zeng X, Wang M, et al. Recommender systems based on social networks. Journal of Systems and Software. 2015;99:109–19.
Lu J, Wu D, Mao M, Wang W, Zhang G. Recommender system application developments: a survey. Decision Support Systems. 2015;74:12–32.
HaiHong E, JianFeng W, MeiNa S, Qiang B, YingYi L. Incremental weighted bipartite algorithm for large-scale recommendation systems. Turkish Journal of Electrical Engineering & Computer Sciences. 2016;24(2).
Cui Z, Xu X, Fei X, Cai X, Cao Y, Zhang W, et al. Personalized recommendation system based on collaborative filtering for IoT scenarios. IEEE Transactions on Services Computing. 2020;13(4):685–95.
Krzywicki A, Wobcke W, Kim YS, Cai X, Bain M, Mahidadia A, et al. Collaborative filtering for people-to-people recommendation in online dating: Data analysis and user trial. International Journal of Human-Computer Studies. 2015;76:50–66.
Kim YS, Mahidadia A, Compton P, Cai X, Bain M, Krzywicki A, et al., editors. People recommendation based on aggregated bidirectional intentions in social network site. Pacific Rim Knowledge Acquisition Workshop; 2010: Springer.
Gurini DF, Gasparetti F, Micarelli A, Sansonetti G. Temporal people-to-people recommendation on social networks with sentiment-based matrix factorization. Future Generation Computer Systems. 2018;78:430–9.
Cai X, Bain M, Krzywicki A, Wobcke W, Kim YS, Compton P, et al., editors. Collaborative filtering for people to people recommendation in social networks. Australasian Joint Conference on Artificial Intelligence; 2010: Springer.
Raghuwanshi SK, Pateriya R. Recommendation Systems: Techniques, Challenges, Application, and Evaluation. Soft Computing for Problem Solving: Springer; 2019. pp. 151–164.
McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics. 1943;5(4): 115–133.
Walczak S. Artificial neural networks. Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction: IGI Global; 2019. pp. 40–53.
Macukow B, editor Neural networks–state of art, brief history, basic models and architecture. IFIP international conference on computer information systems and industrial management; 2016: Springer.
Paradarami TK, Bastian ND, Wightman JL. A hybrid recommender system using artificial neural networks. Expert Systems with Applications. 2017;83:300–313.
Tsai C-F, Hung C. Modeling credit scoring using neural network ensembles. Kybernetes. 2014.
Balabanović M, Shoham Y. Fab: content-based, collaborative recommendation. Communications of the ACM. 1997;40(3):66–72.
Zadeh LA, Klir GJ, Yuan B. Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers: World Scientific; 1996.
Klir G, Yuan B. Fuzzy sets and fuzzy logic: Prentice hall New Jersey; 1995.
Yera R, Castro J, Martínez L. A fuzzy model for managing natural noise in recommender systems. Applied Soft Computing. 2016;40:187–198.
Kermany NR, Alizadeh SH. A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques. Electronic Commerce Research and Applications. 2017;21:50–64.
Lu HE, Potter A, Rodrigues VS, Walker H. Exploring sustainable supply chain management: a social network perspective. Supply Chain Management: An International Journal. 2018.
Ngai EW, Chau DC, Chan T. Information technology, operational, and management competencies for supply chain agility: Findings from case studies. The Journal of Strategic Information Systems. 2011;20(3):232–49.
Palacios-Marques D, Popa S, Mari MPA. The effect of online social networks and competency-based management on innovation capability. Journal of Knowledge Management. 2016.
Hamilton JW, Kwon I-W. Strategic Success, Supply Chain Performance, and Social Media. 2016.
Markova S, Petkovska-Mirèevska T. Social media and supply chain. Amfiteatru Economic Journal. 2013;15(33):89–102.
Lambert DM. Supply chain management: processes, partnerships, performance: Supply Chain Management Inst; 2008.
Cox DD, McLeod S. Social media marketing and communications strategies for school superintendents. Journal of Educational Administration. 2014.
Linkedin. 2021 [Available from: https://about.linkedin.com/].
Wu L, Shah S, Choi S, Tiwari M, Posse C. The browsemaps: Collaborative filtering at LinkedIn. RSWeb@ RecSys. 2014;1271.
Al-Shamri MYH. User profiling approaches for demographic recommender systems. Knowledge-Based Systems. 2016;100:175–187.
Choi K, Suh Y. A new similarity function for selecting neighbors for each target item in collaborative filtering. Knowledge-Based Systems. 2013;37:146–153.
Sert SY, Ar Y, Bostanci GE. Evolutionary approaches for weight optimization in collaborative filtering-based recommender systems. Turkish Journal of Electrical Engineering & Computer Sciences. 2019;27(3):2121–2136.
Sondur MSD, Chigadani MAP, Nayak S. Similarity measures for recommender systems: a comparative study. Journal for Research. 2016;2(3).
Ogbuanya TC, Chukwuedo SO. Career-training mentorship intervention via the Dreyfus model: Implication for career behaviors and practical skills acquisition in vocational electronic technology. Journal of Vocational Behavior. 2017;103:88–105.
Hameed IA, Elhoushy M, Osen OL, editors. Interval Type-2 Fuzzy Logic Systems for Evaluating Students’ Academic Performance. International Conference on Computer Supported Education; 2016: Springer.
Isinkaye F, Folajimi Y, Ojokoh B. Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal. 2015;16(3): 261–273.
Silveira T, Zhang M, Lin X, Liu Y, Ma S. How good your recommender system is? A survey on evaluations in recommendation. International Journal of Machine Learning and Cybernetics. 2019;10(5):813–831.
