NLPIR SEMINAR Y2019#17
In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his/her related research with you.
This week’s seminar is organized as follows:
- The seminar time is 1.pm, Mon, at Zhongguancun Technology Park ,Building 5, 1306.
- The lecturer is Yvette, the paper’s title is Search Engine Optimization Algorithms for Page Ranking: Comparative Study.
- Li Shen will give the presentation of her work.
- The seminar will be hosted by Gang Wang.
- Attachment is the paper of this seminar, please download in advance.
Everyone interested in this topic is welcomed to join us. the following is the abstract for this week’s paper.
Search Engine Optimization Algorithms for Page Ranking: Comparative Study
Arif Ullah1, Nazri Mohd Nawi, Edi Sutoyo, Asim Shazad, Sundas Naqeeb Khan, Muhammad Aamir
No doubt that every second in our daily routine, the number of visitors that connect to the internet increase day by day due to the fast growing of World Wide Web. Until this day there are more than 11.3 billion web pages in the World Wide Web. In the modern era of technology and advance computation, world page ranking is becoming a common feature of modern retrieval system. However, many of us did not realise that any query in search engine will display both relevant and irrelevant data that can cause overhead to the search engine and will affect the page ranking process. Therefore, a new optimization technique to improve the existing search engine optimization in increasing the page ranking is needed. This paper presents a review and comparative study of different existing page ranking algorithms for search engine optimization. This paper also explores some improvements that are needed to overcome the current problem in this field by testing on some real case data. The simulation result’s analysis clearly shows that there is a need for new optimization technique which can reduce the complexity and user overhead by displaying only related data which will reduce over heading in search engine.