Model for Re-ranking Agent on Hybrid search engine for E-Learning

Abstract—The Web provides an enormous amount of learning tutorials. Searching standard content is not easy for common user of traditional search engine. The user only focuses on the top results from the enormous quantity of the arrived results. So the Re-ranking problem turns into the significant responsibility for the search systems. This paper proposes a novel model of Re-ranking Agent on Hybrid search engine (Meta-search engine and Topical search engine) for helping learners searching online Learning tutorials efficiently and in the effective way. With the aim of providing users with relevant tutorials we have proposed model classified by subject topic, prepared by professional persons and preferred by learners.

Keywords- Search engine, E-learning Web Page, Feedback using Web Log, Meta tag author

I. INTRODUCTION Huge quantities of E-learning tutorial in a form of web

pages are steadily accumulating on the World Wide Web. Usage of online materials is slowly and gradually increasing in today’s world. Few First-Tier search engines like Google, Yahoo etc… share the market share among various crawler based search engines. Web crawler based search engine will give different search results for an identical topic. It is detrimental for the user to visit multiple crawler based search engines. Solution of this dilemma is to use Hybrid Search engine combination of Topical and Meta-search engine. Topical Search engine also referred as specialty search engine focuses on a specific topic like business, academia etc… and Meta-search engine which does not hold on its own index directory but it believes on crawler based search engine to improve the time efficiency and quality of search results.

The user only focuses on the top results from the enormous quantity of the arrived results. So the Re-ranking problem turns into the significant responsibility for the search systems. A Re-ranking Agent on Hybrid search engine turns out to be an intelligent Hybrid search engine technology towards the evolution in search engine. This Paper proposes an evolutionary model for Re-ranking Agent on Meta-Search Engine for E-Learning that will benefit by providing quality learning tutorial and utilizing time of expert.

II. RELATED RESEARCH Researchers have suggested certain customizations on

Re-ranking of search engine result pages. Chen and Liu used the concept of Meta-search engine on Agent [7].

By using the meta-search engine technologies, they fulfill user’s request well by increasing the precision rate and the recall rate of traditional search engines.

Zhao et. al. presented a new search ranking algorithm bases on web pages and tags clustering, and use several evaluating methods and proved that the tag on web pages are really helpful for improving the experience of user [8].

Use of tag, title and author from the learning document also improves the performance of search engine results [10]. Arrigo et. al. introduced an iterative technique to build a taxonomy which is used to classify documents regarding a specific topic [9]. It also mentions that most of search engines are keyword based. After selecting document based on keyword, it is required to check relevancy of topic. Decision Tree classification technique of data mining is chosen to classify documents [6].

ConnectA [3] helps the surfers search the web to find the documents of an authors’ community around a specific research area. CRANAI has used author’s inputs by combining a hyperlink-based page ranking algorithm [11].

It is good to take the feedback of the user to know their satisfaction in search result. Several authors have considered feedback as a one component in their customized search engine. Survey of User Behaviors as Implicit Feedback was conducted and suggested that implicit techniques have a lower cost compared with the traditional explicit feedback [1], [2]. Clickthrough data, namely the query-log of the search engine is available in abundance and can be recorded at very low cost for re-ranking of search results [5].

Sweetsearch, powered by Google is a search that returns results that are accurate, reliable, safe, and understandable. It contains sites that have been evaluated and approved by a staff of Internet research experts [12]. In the Sweetsearch search engine Subject experts evaluate and approve/reject the content but the process is time consuming.

Thus, this Paper proposes a novel model for re-ranking Agent on meta-search engine for e-learning that will benefit by providing quality learning tutorial and utilizing time of expert. The objective of this proposed model is to provide users with relevant tutorials classified by subject topic, prepared by professional persons and preferred by learners.

III. PROPOSED MODEL The Proposed Model of the system comprises A)

Hybrid Search Engine and B) Re-ranking Agent. Fig. 1 shows the proposed model diagram for Re-ranking Agent on Meta-search engine for E-Learning. The working of each of the module is discussed in the following sections.

A. Hybrid Search Engine Hybrid Search Engine consists of Topical and Meta

search engine passes the Query passed by user to the First-

Axita Shah AES Institute of

Computer Studies Ahmedabad, India

axitashah@gmail.com

Dr. Sonal Jain GLS Institute of

Computer Application Ahmedabad, India

sonal_sethia@yahoo.com

Rushabh Chheda AES Institute of Computer

Studies Ahmedabad, India

rushabhlchheda@gmail.com

Avni Mashru AES Institute of Computer

Studies Ahmedabad, India

mashru.avni@gmail.com

2012 IEEE Fourth International Conference on Technology for Education

978-0-7695-4759-6/12 $26.00 © 2012 IEEE

DOI 10.1109/T4E.2012.55

247

Tier Search Engine. Topical search engine takes care of education related learning tutorial. To improve the result, First-Tier Search engines are used to retrieve the results.

For all intents and purposes, most of the visitors will come from First-Tier or primary search engines [4] [12]. The study suggests that Meta search engine which believes on First-Tier search engine will give better quality result than second tier search engine

Top 20 distinct SERPs (Search Engine Result Pages) as an output of the Hybrid Search Engine will be considered as an input to Re-ranking Agent.

B. Re-ranking Agent The Re-ranking Agent generates Customized SERPs

and provides to the E-learners. The process of Re-ranking Agent is divided into three modules. The working of each of the three modules is discussed in the following sections.

1) Consideration of Author’s Profile: To consider subject expert’s tutorial as a priority, First part of the Agent gives a novice idea to believe on author’s profile. Two aspects of the author are very significant i.e. author’s education degree and experience. Every time an author loads the tutorial on the web, it is recommended to insert author’s experience in years and highest degree of education. Author’s profile will be added inside the author attribute of meta tag in HTML web page. Tag structure: <meta name=”author” content=”Name of the Author, Experience in no. of years, highest degree”/> Author’s profile consideration will be effective and speedy.

2) Classification of Tutorial: Classification based on keywords of tutorial will group similar web pages collectively. There are many classification data mining techniques like Bayesian classification, Decision Tree, Support Vector Machine, Backpropagation etc… are available. Further work will include the study of classification techniques to select best suited one. This technique will classify the tutorial in one class group using

Figure 1. Re-Ranking Agent on Hybrid Search Engine for E-Learning

collected keywords from the web page. 3) Feedback Analysis: Feedback of the user is

considered significant to analyze and improve the result of search engine. In today’s world very few learners are ready to give an explicit feedback. So, we will consider implicit feedback of the user from the web log. Researchers have worked on several web usage mining techniques to analyze on learner’s web history log data. Further work will include to study on web usage mining techniques to decide one optimized technique.

IV. CONCLUSION AND FUTURE WORK We have included novice idea of author’s profile to be

inserted inside the Meta tag of HTML tutorial. The prime focus of this model is to generate appropriate and relevant learning documents that too with preferred, accurate and quality web pages for all E-Learners. Our work would explore each and every modules of Agent to get efficient result of model. Future work will include study of Classification techniques and web usage mining techniques. To prove the model, implementation and analysis of the proposed model will be made to complete.

REFERENCES [1] B. Zhang, Y. Guan, H. Sun, Q. Liu, and J. Kong, "Survey of User

Behaviors as Implicit Feedback," in Computer, Mechatronics, Control and Electronic Engineering (CMCE), Changchun, IEEE, vol.6, Aug2010,pp. 345–348,doi: 10.1109/CMCE.2010.5609830.

[2] E. Agichtein, E. Brill and Susan Dumais, "Improving Web Search Ranking by Incorporating," in Research and development in information retrieval,Montreal Que, ACM, Oct7-10. 2007,pp.3075 – 3080,doi: 10.1109/ICSMC.2007.4414122.

[3] H. Baghi, M. Barouni-Ebrahimi, A. A. Ghorbani and R. Z., "ConnectA!: An Intelligent Search Engine based on Authors’ Connectivity," Communication Networks and Services Research, IEEE press, 2007, pp.133–140, doi:10.1109/CNSR.2007.26.

[4] J. I. Jerkovic, SEO Warrior, O'Reilly, November 2009. [5] T. Joachims, "Optimizing Search Engines using Clickthrough

Data," in SIGKDD, Edmonton, Alberta, Canada, ACM, 2002,doi:10.1145/775047.775067

[6] C. J. Liu Wei, "Design and implementation of an intelligent metasearch engine based on agent," International Journal of Agent-Oriented Software Engineering, Inderscience Publishers, Geneva, SWITZERLAND,vol.1 Issue 2, July 2007,doi:10.1504/IJAOSE.2007.014402.

[7] J. Chen and W. Liu, "A Framework for intelligent meta-search Engine based on Agent," in Proceedings of the Third International Conference on Information Technology and Applications (ICITA’05),IEEE, vol.1,July2005,pp. 276 - 279 vol.1,doi: 10.1109/ICITA.2005.16.

[8] C. Zhao, Z. Zhan and H. Li, X, Xie, “A Search Result Ranking Algorithm Based on Web Pages and Tags Clustering”, Shanghai,June 2011,IEEEpp:609-614,doi:10.1109/CSAE.2011.5952922

[9] M. Arrigo, M. Gentile,D. Taibi, and O. Di Giuseppe, "Specialized Search Engines for E-learning," Via Ugo la Malfa, 153 - 90146 Palermo, ITALY, 2005,pp:1-5.

[10] P. Jomsri, S. Sanguansintukul, and W. Choochaiwattana, “A Comparison of Search Engine Using -Tag Title and Abstract with CiteULike” – An Initial Evaluation, London, IEEE, Nov.2009, pp:1-5.

[11] J. Lai and B. Soh, "CRANAI: A New Search Model Reinforced by Combining a Ranking," in e-Business Engineering (ICEBE’05),Beijing, IEEE,Oct.2005,pp:340-345,doi:10.1109/ICEBE.2005.44.

[12] Sweet Search, online at http://www.sweetsearch.com , (2012).

248