A possiblistic clustering based biased Bayesian relevance feedback model for web usage recommendation

  • Authors

    • Sumanth Venugopal RAJAREDDY INSTITUTE OF TECHNOLOGY
    • Guruprasad Nagraj NEW HORIZON COLLEGE OF ENGINEERING
    2018-12-17
    https://doi.org/10.14419/ijet.v7i4.16224
  • Possibility Fuzzy C-Means, Recommendation System, Relevance Feedback Biased Bayesian, Web Usage Mining.

  • World Wide Web (WWW) consists of a huge amount of web pages and links, provides massive information to the internet users. The de-velopments of websites have become challenging thing and size of web contents is more abundant. The web usage mining technique is em-ployed in web server log for extracting the user information. Presently, the Web Recommendation System (RS) is rapidly developing and major objective is generating the customized data for the end users. The RS is the platform that make personalized recommendations for a particular user by predicting the ratings for different items. In this paper, an efficient web RS that consists of two methods such as Possibil-istic Fuzzy C-Means (PFCM) and Relevance Feedback Biased Bayesian Network (RFBBN) methods are proposed. The PFCM algorithm clusters the similar web page users. In these clusters RFBBN model extract the relevant information and predict the relevant web pages. The proposed method reduces the loss of the end users. The experimental analysis demonstrated that the PFCM-RFBBN approach delivered the high priority of web pages and also recommended the related web pages. Finally, the experimental outcome showed that the proposed ap-proach improved accuracy in web page recommendation up to 31% compared to the existing methods.

     

     

     

  • References

    1. [1] Tewari AS, & Barman AG (2018), “Sequencing of items in personalized recommendations using multiple recommendation techniques,†Expert Systems with Applications, Vol. 97, pp. 70-82. https://doi.org/10.1016/j.eswa.2017.12.019.

      [2] Seo, YD, Kim YG, Lee E, Seol KS, & Baik DK (2018), An enhanced aggregation method considering deviations for a group recommendation,†Expert Systems with Applications, Vol. 93, pp. 299-312. https://doi.org/10.1016/j.eswa.2017.10.027.

      [3] Thiyagarajan R, Thangavel K, & Rathipriya R (2014), “Recommendation of web pages using weighted K-means clustering,†International Journal of Computer Applications, Vol. 86, No. 14.

      [4] Duwairi R, & Ammari H (2016), “An enhanced CBAR algorithm for improving recommendation systems accuracy,†Simulation Modelling Practice and Theory, Vol. 60, pp. 54-68. https://doi.org/10.1016/j.simpat.2015.10.001.

      [5] Zheng L, Qu Y, Qian X, & Cheng G (2018), “A hierarchical co-clustering approach for entity exploration over Linked Data,†Knowledge-Based Systems, Vol. 141, pp. 200-210. https://doi.org/10.1016/j.knosys.2017.11.017.

      [6] Adeniyi DA, Wei Z, & Yongquan Y (2016), “Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method,†Applied Computing and Informatics, Vol. 12, No. 1, pp. 90-108. https://doi.org/10.1016/j.aci.2014.10.001.

      [7] Chawla S (2016), “A novel approach of cluster based optimal ranking of clicked URLs using genetic algorithm for effective personalized web search,†Applied Soft Computing, Vol. 46, pp. 90-103. https://doi.org/10.1016/j.asoc.2016.04.042.

      [8] Li H, Xu Z, Li T, Sun G, & Choo KKR (2017), “An optimized approach for massive web page classification using entity similarity based on semantic network,†Future Generation Computer Systems, Vol. 76, pp. 510-518. https://doi.org/10.1016/j.future.2017.03.003.

      [9] Xie X, & Wang B (2018), “Web page recommendation via twofold clustering: considering user behavior and topic relation,†Neural Computing and Applications, Vol. 29, No. 1, pp. 235-243. https://doi.org/10.1007/s00521-016-2444-z.

      [10] Xuan J, Luo X, Zhang G, Lu J, & Xu Z, “Uncertainty analysis for the keyword system of web events,†IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 46, No. 6, pp. 829-842. https://doi.org/10.1109/TSMC.2015.2470645.

      [11] Husamuddin Md, & Mazarbhuiya FA (2015), “Clustering of Locally Frequent Patterns Over Fuzzy Temporal Datasets,†International Journal of Computer Trends and Technology, Vol. 28, No. 3, pp. 131-134. https://doi.org/10.14445/22312803/IJCTT-V28P124.

      [12] Deng L, & Li D (2017), “Multimedia data stream information mining algorithm based on jointed neural network and soft clustering,†Multimedia Tools and Applications, pp. 1-24. https://doi.org/10.1007/s11042-017-4964-7.

      [13] Wagh RB, & Patil JB (2017), “Enhanced Web Personalization for Improved Browsing Experience,†Advances in Computational Sciences and Technology, Vol. 10, No. 6, pp. 1953-1968.

      [14] Damodharan P, & Ravichandran CS (2016), “Certain Investigation on Automatic Recommendation for Online Users Using Web Usage Mining,†Certain Investigation on Automatic Recommendation for Online Users Using Web Usage Mining, Vol. 1, No. 1, pp. 72-77.

      [15] Anandhi D, & Ahmed MSI, “Prediction of user’s type and navigation pattern using clustering and classification algorithms,†Cluster Computing, pp. 1-10, 2017.

      [16] Forsati R, Moayedikia A, & Shamsfard M (2015), “An effective Web page recommender using binary data clustering,†Information Retrieval Journal, Vol. 18, No. 3, pp. 167-214. https://doi.org/10.1007/s10791-015-9252-4.

      [17] Katarya R, & Verma OP (2017), “An effective web page recommender system with fuzzy c-mean clustering,†Multimedia Tools and Applications, Vol. 76, No. 20, pp. 21481-21496. https://doi.org/10.1007/s11042-016-4078-7.

      [18] Sahoo S, & Ratha BK (2018), “Rapid Frequent Pattern Growth and Possibilistic Fuzzy C-means Algorithms for Improving the User Profiling Personalized Web Page Recommendation System,†International Journal of Intelligent Engineering and Systems, Vol. 11, No. 2.

      [19] Mishra R, Kumar P, & Bhasker B (2015), “A web recommendation system considering sequential information,†Decision Support Systems, Vol. 75, pp. 1-10. https://doi.org/10.1016/j.dss.2015.04.004.

      [20] Nigam B, Tokekar S, & Jain S (2015), “Evaluation of models for predicting user's next request in web usage mining,†International Journal on Cybernetics & informatics (UCi), Vol. 4, pp. 1-13. https://doi.org/10.5121/ijci.2015.4101.

  • Downloads

  • How to Cite

    Venugopal, S., & Nagraj, G. (2018). A possiblistic clustering based biased Bayesian relevance feedback model for web usage recommendation. International Journal of Engineering & Technology, 7(4), 4024-4029. https://doi.org/10.14419/ijet.v7i4.16224