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RETRACTED ARTICLE: Framework for social tag recommendation using Lion Optimization Algorithm and collaborative filtering techniques

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This article was retracted on 30 November 2022

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Abstract

Recommendation systems have been paying attention as gaining a much important character with the growth of data mining with collaborative filtering (CF) techniques. With a specific end goal to perform better recommendation data mining and collaborative filtering methodologies are used these days. The most favourite technique behind the success of the recommendation system was collaborative filtering. CF promise the interested of an active user supported on the sentiment of users with correspondent interests. Data mining techniques lead to the reduction of huge data set into smaller data set in which all the services are similar to one another. To recommend social tag we proposed a framework that is combining the data mining techniques such as feature selection and clustering with collaborative filtering algorithms. In this paper lion optimization technique are utilized for feature selection and clustering and it was hybridized with slope one algorithm. At long last, this calculation is contrasted and slope one calculation and the execution is dissected by utilizing the measurements such as precision, recall, mean absolute error and root mean square error.

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References

  1. Kumar, S.S., Inbarani, H.H.: Web 2.0 social bookmark selection for tag clustering. In: Proceedings of International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME), Periyar University, Salem, IEEE, pp. 510–516 (2013)

  2. Selvakumar, S., Inbarani, H., Shakeel, P.M.: A hybrid personalized tag recommendations for social e-learning system. Int. J. Control Theory Appl. 9(2), 1187–1199 (2016)

    Google Scholar 

  3. Inbarani, H.H., Kumar, S.S.: Hybrid tolerance rough set based intelligent approaches for social tagging systems. Big Data in Complex Systems: Challenges and Opportunities. Studies in Big Data, Springer, Berlin, vol. 9(1), pp. 231–261 (2015)

  4. Vijaya Kumar, P.N., Raghunatha Reddy, V.: A survey on recommender systems (RSS) and its applications. Int. J. Innov. Res. Comput. Commun. Eng. 2(8), 5254–5260 (2014)

    Google Scholar 

  5. Baskar, S., Periyanayagi, S., Shakeel, P.M., Dhulipala, V.S.: An energy persistent range-dependent regulated transmission communication model for vehicular network applications. Comput. Netw. (2019). https://doi.org/10.1016/j.comnet.2019.01.027

    Article  Google Scholar 

  6. Pan, J., Abidi, A.A., Jiang, W., Marković, D.: Simultaneous transmission of up to 94-mW self-regulated wireless power and up to 5-Mb/s reverse data over a single pair of coils. IEEE J. Solid-State Circ. 54(4), 1003–1016 (2019)

    Article  Google Scholar 

  7. Polak, R., Lubkowski, P., Sierzputowski, R., Wojtyra, D., Laskowski, D.: Secure voice transmission in the coalitional communication. In: XII Conference on Reconnaissance and Electronic Warfare Systems. International Society for Optics and Photonics, vol. 11055, p. 110550R (2019)

  8. Isinkaye, F.O.: Recommendation systems: principles, methods, and evaluation. Egypt. Inf. J. 16(3), 261–273 (2015)

    Google Scholar 

  9. Romero, C., Ventura, S., Zafra Paul de Bra, A.: Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems. Comput. Educ. 53(3), 828–840 (2009)

    Article  Google Scholar 

  10. Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized recommendation in social tagging systems using hierarchical clustering. In: RecSys ‘08 Proceedings of the ACM Conference on Recommender Systems, pp. 259–266 (2008)

  11. Mai, J., Fan, Y., Shen, Y.: A neural networks-based clustering collaborative filtering algorithm in E-commerce recommendation system. In: Proceedings of International Conference on Web Information Systems and Mining, pp. 616–619, IEEE (2009)

  12. Gong, S.J., Ye, H.W., Su, P.: A peer-to-peer based distributed collaborative filtering architecture. In: Proceedings of International Joint Conference on Artificial Intelligence, IEEE, pp. 305–307 (2009)

  13. Birtolo, C., Ronca, D.: Advances in clustering collaborative filtering by means of Fuzzy C-means and trust. Expert Syst. Appl. 40(17), 6997–7009 (2013)

    Article  Google Scholar 

  14. Kazik, O., Pekov, K., Pilat, M., Neruda, R.: A novel meta-learning system and its application to optimization of computing agents’ Results. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 02, pp. 170–174 (2012)

  15. Satapathy, S., Parvathi, K.: Unsupervised feature selection using rough set and teaching learning-based optimization. Int. J. Artif. Intell. Soft Comput. 3(3), 244–256 (2013)

    Google Scholar 

  16. Zhou, M., Wang, F., Zimmerman, T., Liang, H., Haber, E., Gou, L.: Computational discovery of personal traits from social multimedia. In: IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1–6 (2013)

  17. Cho, Y.S., Moon, S.C., Noh, S.C., Ryu, K.H.: Implementation of personalized recommendation system using k-means clustering of item category based on RFM. In: Proceedings of IEEE International Conference on Management of Innovation and Technology (ICMIT), pp. 378–383 (2012)

  18. Zhang, Z.-K., Zhou, T., Zhang, Y.-C.: Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs. Phys. A 389(1), 179–186 (2013)

    Article  Google Scholar 

  19. Tsai, C.-F.: Combining cluster analysis with classifier ensembles to predict financial distress. Inf. Fusion 16, 46–58 (2014)

    Article  Google Scholar 

  20. Mustapha, N., Voon, W.P., Sulaiman, N.: User recommendation algorithm in social tagging system based on hybrid user trust. J. Comput. Sci. 9(8), 1008–1018 (2013)

    Article  Google Scholar 

  21. Preeth, S.K.S.L., Dhanalakshmi, R., Kumar, R., Shakeel, P.M.: An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system. J. Ambient Intell. Hum. Comput. (2018). https://doi.org/10.1007/s12652-018-1154-z

    Article  Google Scholar 

  22. Jayasree, P., Suganya, G., Kumar, A.C.D.N.: Query-based recommendation and Gaussian firefly based clustering algorithm for inferring user feedback sessions with search goals. Int. J. Comput. Appl. 114(2), 14–19 (2015)

    Google Scholar 

  23. MuhammedShafi, P., Selvakumar, S., Mohamed Shakeel, P.: An efficient optimal fuzzy C means (OFCM) algorithm with particle swarm optimization (PSO) to analyze and predict crime data. J. Adv. Res. Dyn. Control Syst. 06, 699–707 (2018)

    Google Scholar 

  24. Htun, Z., Tar, P.P.: A resource recommender system based on social tagging data. Mach. Learn. Appl. 1(1), 1–11 (2014)

    Google Scholar 

  25. Wen, J., Zhou, W.: An improved item-based collaborative filtering algorithm based on clustering method. J. Comput. Inf. Syst. 8(2), 571–578 (2012)

    Google Scholar 

  26. Sajwan, M., Acharya, K., Bhargava, S.: Swarm intelligence based optimization for web usage mining in recommender system. Int. J. Comput. Appl. Technol. Res. 3(2), 119–124 (2014)

    Google Scholar 

  27. Selva Kumar, S., Hannah Inbarani, H.: Hybrid TRS-FA clustering approach for Web2.0 social tagging system. Int. J. Rough Sets Data Anal. 2(1), 70–87 (2015)

    Article  Google Scholar 

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Correspondence to Minghui Wang.

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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s10586-022-03840-8

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Wang, T., Manogaran, G. & Wang, M. RETRACTED ARTICLE: Framework for social tag recommendation using Lion Optimization Algorithm and collaborative filtering techniques. Cluster Comput 23, 2009–2019 (2020). https://doi.org/10.1007/s10586-019-02980-8

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