A New Hybrid Method for Web Pages Ranking in Search Engines

Subject Areas : Knowledge Engineering

Sajjad Najafi ¹ , Farhad Soleimanian Gharehchopogh ²

1 - Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
2 - Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, IRAN

Received: 2019-06-15 Accepted : 2019-08-01 Published : 2019-11-01

Keywords: Ranking, Search Engine, Distance Rank, User Attention Time, Click through Rate,

Abstract :

There are many algorithms for optimizing the search engine results, ranking takes place according to one or more parameters such as; Backward Links, Forward Links, Content, click through rate and etc. The quality and performance of these algorithms depend on the listed parameters. The ranking is one of the most important components of the search engine that represents the degree of the vitality of a web page. It also examines the relevance of search results with the user's query. In this paper, we try to optimize the search engine results ranking by using the hybrid of the structure-based algorithms (Distance Rank algorithm) and user feedback-based algorithms (Time Rank algorithm). The proposed method acts on multiple parameters and with more parameters it tries to get better results while keeping the complexity and running time of the algorithms. Average distance and average attention time have been evaluated on web pages and by using the obtained data, proposed method performance has been evaluated. We compare proposed method with several famous algorithms such as Time Rank, Page Rank, R Rank, WPR and sNorm(p) in this field by applying Precision@N (P@N), Average Precision (AP), Mean Reciprocal Rank (MRR), Mean Average Precision (MAP), Discounted Cumulative Gain (DCG) and Normalized Discounted Cumulative Gain (NDCG) criteria. The results indicate better performance in comparison with existing algorithms.

References:

[1] S. Khalatbari and S.A. Mirroshandel, "Automatic construction of domain ontology using wikipedia and enhancing it by google search engine," Journal of Information Systems and Telecommunication, vol. 3, no. 4, 2015, pp. 248-258.
[2] S. Chawla, "A novel approach of cluster based optimal ranking of clicked URLs using genetic algorithm for effective personalized web search," Applied Soft Computing, vol. 46, 2016, pp. 90-103.
[3] F.S. Gharehchopogh, & Z.A. Khalifelu, (2011, October). Analysis and evaluation of unstructured data: text mining versus natural language processing. In 2011 5th International Conference on Application of Information and Communication Technologies (AICT) (pp. 1-4). IEEE.
[4] Divjot and J. Singh, "Effective Model And Implementation Of Dynamic Ranking In Web Pages," 2015 Fifth International Conference on Communication Systems and Network Technologies, 2015, pp. 1010-1014.
[5] R. Chaudhary and M. Bhusry, "A New Contrive to Evaluate Web Page Ranking," Ajay Kumar Garg Engineering College Ghaziabad, India, 2014, pp. 1-6.
[6] P. Kumari, P. Ranout , A. Sharma and P. Sharma, "Web Mining - Concept, Classification and Major Research Issues: A Review," 1, 2, 3 & 4 Deptt. of Computer Science and Engineering, Career Point University, Hamirpur,(H.P.) INDIA, 2016, pp. 41-44.
[7] S. Viralkumar M, R. J. Patel and . N. Kumar Singh, "Web Mining: A Survey on Various Web Page Ranking Algorithms," International Research Journal of Engineering and Technology (IRJET), vol. 03, no. 04, 2016, pp. 1206-1211.
[8] G. M. Salton, A. Wong and. C. Yang, "A Vectore Space Model for Automatic Indexing," Information Retrieval and Language Processing, vol. 18, 1975, pp. 613-620.
[9] Langville, A.N., & Meyer, C.D. (2011). Google's PageRank and beyond: the science of search engine rankings. Princeton University Press..
[10] J.M. Kleinberg, "Authoritative sources in a hyperlinked environment," Journal of the ACM, vol. 46, no. 5, 1999, p. 604–632.
[11] T. joachims, "optimizing search engine using clickthrough data," department of computer science, 2002, pp. 133-142.
[12] C. K. a. K. Ramamohanarao, "Long-Term Learning for Web Search Engines," Department of Computer Science & Software Engineering, vol. 2431, 2002, p. 263–274.
[13] F. Soleimanian Gharehchopogh, M. Mahmoodi Tabrizi and I. Maleki, "Search Engine Optimization based on Effective Factors of," International Journal of Computer & Mathematical Sciences, vol. 2, no. 1, 2014, pp. 9-13.
[14] A. Shakery and C. Zhai, "Relevance Propagation for Topic Distillation," Department of Computer Science University of Illinois at Urbana-Champaign, 2003, pp. 673-677.
[15] V. derhami, j. Paksima and h. Khajeh, "Web pages ranking algorithm based on reinforcement learning and userfeedback." Journal of AI and Data Mining, vol. 3, no. 2, 2014, pp. 157-168.
[16] W. Xing and A. Ghorbani, "Weighted PageRank Algorithm," Proceedings of the Second Annual Conference on Communication Networks and Services Research, 2004, pp. 305-314.
[17] A. M. Zareh Bidoki and N. Yazdani, "DistanceRank: An intelligent ranking algorithm," Information processing & management, vol. 44, no. 2, 2007, p. 877–892.
[18] Y. Z. H. J. F. C. L. Songhua Xu, "A User-Oriented Webpage Ranking Algorithm Based on User Attention Time," Proceeding AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence, 2008, vol. 2, pp. 1255-1260.
[19] G.-R. Xue, H.-J. Zeng, Z. Chen, Y. Yu, W.-Y. Ma, W. Xi and W. Fan, "Optimizing Web Search Using Web Click-through Data," Proceedings of the thirteenth ACM international conference on Information and knowledge management, 2004, pp. 118-126.
[20] S. Hariharan, S. Dhanasekar and K. Desikan, "Reachability Based Web Page Ranking Using Wavelets," 2nd International Symposium on Big Data and Cloud Computing (ISBCC’15) , 2015, vol. 50, pp. 157-162.
[21] A. Shakery and C. Zhai, "A Probabilistic Relevance Propagation Model for," Department of Computer Science University of Illinois at Urbana-Champaign Illinois, 2006, pp. 550-558.
[22] Kim, D.J., Lee, S.C., Son, H.Y., Kim, S.W. and Lee, J.B., 2014. C-Rank and its variants: A contribution-based ranking approach exploiting links and content. Journal of Information Science, 40(6), pp.761-778.
[23] Koo, Jangwan, Dong-Kyu Chae, Dong-Jin Kim, and Sang-Wook Kim. "Incremental C-Rank: An effective and efficient ranking algorithm for dynamic Web environments." Knowledge-Based Systems 176 (2019): 147-158.
[24] Goel, Shubham, Ravinder Kumar, Munish Kumar, and Vikram Chopra. "An efficient page ranking approach based on vector norms using sNorm (p) algorithm." Information Processing & Management 56, no. 3 (2019): 1053-1066.
[25] Lempel, R. and Moran, S., 2000. The stochastic approach for link-structure analysis (SALSA) and the TKC effect. Computer Networks, 33(1-6), pp.387-401.
[26] Paksima, Javad and Homa Khajeh "The surfer model with a combined approach to ranking the web pages," journal of information systems and telecommunication (JIST) , vol. 4, no. 3, 2016, pp. 200-209.
[27] K. Jarvelin and J. Kekalainen, "IR evaluation methods for retrieving highly relevant," Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002, pp. 41-48.