Web Page Recommendation System Using Laplace Correction Dependent Probability and Chronological Dragonfly-Based Clustering


  • Jyoti Narayan Jadhav
  • B Arunkumar






Web page recommendation, dragonfly algorithm, history of pages, laplacian correction, recommendation probability.


The web page recommenders predict and recommend the web pages to the users based on the behavior of their search history. The web page recommender system analyzes the semantics of the navigation by the user and predicts the related web pages for the user. Various recommender systems have been developed in the literature for the web page recommendation. In the first work, a web page recommendation system was developed using weighted sequential pattern mining and Wu and Li Index Fuzzy clustering (WLI-FC) algorithm. In this work, the Chronological based Dragonfly Algorithm (Chronological-DA) is proposed for recommending the webpage to the users. The proposed Chronological-DA algorithm includes the concept of the chronological for recommending the webpage based on the history of pages visited by the users. Also, the proposed recommendation system uses the concept of Laplacian correction for defining the recommendation probability. Simulation of the proposed webpage recommendation system with the chronological-DA uses the standard CTI and the MSNBC database for the experimentation, and the experimental results prove that the proposed scheme has better values of 1, 0.964, and 0.973 for precision, recall, and F-measure respectively.




[1] Adeniyi DA, Wei Z & Yongquan Y, “Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification methodâ€, Applied Computing and Informatics, Vol.12, No.1, (2016), pp.90–108.

[2] Nguyen TTS, Lu HY & Lu J, “Web-page recommendation based on web usage and domain knowledgeâ€, IEEE Transactions on Knowledge and Data Engineering, Vol.26, No.10,(2014), pp.2574-2587.

[3] Z Yesembayeva (2018). Determination of the pedagogical conditions for forming the readiness of future primary school teachers, Opción, Año 33. 475-499

[4] G Mussabekova, S Chakanova, A Boranbayeva, A Utebayeva, K Kazybaeva, K Alshynbaev (2018). Structural conceptual model of forming readiness for innovative activity of future teachers in general education school. Opción, Año 33. 217-240

[5] Katarya R & Verma OP, “An effective web page recommender system with fuzzy c-mean clusteringâ€, Multimedia Tools and Applications, Vol.76, No.20, (2017), pp.21481-21496.

[6] Do Couto ABG & Gomes LFAM, “Multi-criteria Web Mining with DRSAâ€, Procedia Computer Science, Vol.91, (2016), pp.131-140.

[7] Duwairi R & Ammari H, “An enhanced CBAR algorithm for improving recommendation systems accuracyâ€, Simulation Modelling Practice and Theory, Vol.60, (2016), pp.54-68.

[8] Li H, Xu Z, Li T, Sun G & Choo KKR, “An optimized approach for massive web page classification using entity similarity based on semantic networkâ€, Future Generation Computer Systems, Vol.76, (2017), pp.510-518.

[9] Manohar E & Punithavathani DS, “Hybrid Data Aggregation Technique to Categorize the Web Users to Discover Knowledge About the Web Usersâ€, Wireless Personal Communications, Vol.97, No.4, (2017), pp.5289–5303.

[10] Zhang S, Zhang S, Yen NY & Zhu G, “The Recommendation System of Micro-Blog Topic Based on User Clusteringâ€, Mobile Networks and Applications, Vol.22, No.2, (2017), pp.228-239.

[11] Serrano W & Gelenbe E, “The Random Neural Network in a Neurocomputing Application for Web Searchâ€, Neurocomputing, (2017).

[12] Castellano G, Fanelli AM & Torsello MA, “NEWER: A system for NEuro-fuzzy WEb Recommendationâ€, Applied Soft Computing, Vol.11, No.1, (2011), pp.793-806.

[13] Göksedef M & Gündüz-Öğüdücü Åž, “Combination of Web page recommender systemsâ€, Expert Systems with Applications, Vol.37, No.4, (2010), pp.2911-2922.

[14] Mishra R, Kumar P & Bhasker B, “A web recommendation system considering sequential informationâ€, Decision Support Systems, Vol.75, (2015), pp.1-10.

[15] Wu S, Jiang M, Gao X & Wei G, “Webpage Recommender System concerning high dimensional and sparse featuresâ€, Proceedings of 8th International Conference on Information Science and Digital Content Technology, (2012), pp.109-112.

[16] Wang C, Kalra A, Borcea C & Chen Y, “Revenue-Optimized Webpage Recommendationâ€, Proceedings of IEEE International Conference on Data Mining Workshop, (2015), pp.1558-1559.

[17] Sejal D, Kamalakant T, Tejaswi V, Anvekar D, Venugopal KR, Iyengar SS & Patnaik LM, “WNPWR: Web navigation prediction framework for webpage recommendationâ€, Proceedings of IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), (2015), pp.120-125.

[18] Kolekar P & Wakhade S, “A novel approach to provide Web page recommendation using domain knowledge and web usage knowledgeâ€, Proceedings of International Conference on Communication and Electronics Systems, (2016), pp.1-5.

[19] Su AJ, Hu YC, Kuzmanovic A & Koh CK, “How to Improve Your Google Ranking: Myths and Realityâ€, Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, (2010), pp.50-57.

[20] Chiu PH, Kao GYM & Lo CC, “Personalized blog content recommender system for mobile phone usersâ€, International Journal of Human-Computer Studies, Vol.68, No.8, (2010), pp.496-507.

[21] Zheng N & Li Q, “A recommender system based on tag and time information for social tagging systemsâ€, Expert Systems with Applications, Vol.38, No.4, (2011), pp.4575-4587.

[22] Mirjalili S, “Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problemsâ€, Neural Computing and Applications, Vol.27, No.4, (2016), pp.1053–1073.

[23] Störr HP, Xu Y & Choi J, “A compact fuzzy extension of the Naive Bayesian classification algorithmâ€, Proceedings In Tech/VJ Fuzzy, (2002), pp.172-177.

[24] Jadhav JN & Asaithambi M, “Web Page Recommendation System Using Weighted Sequential Pattern Mining and WLI Fuzzy Clusteringâ€, Journal of Advanced Research in Dynamical and Control Systems, (2017), pp.42-59.

[25] CTI dataset from facweb.cs.depaul.edu/mobasher/classes/ect584/ resource.html, 2017.

[26] MSNBC dataset from https://archive.ics.uci.edu/ml/machine-learning-databases/msnbc-mld/msnbc.data.html, 2017.

[27] Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U & Hsu MC, “Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growthâ€, Proceedings of the 17th international conference on data engineering, (2001).

[28] Ren JD & Sun YF, “Interactive mining of Maximal Constrained frequent Patternsâ€, Database Engineering and Applications Symposium, (2004).

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