Time and location based summarized PageRank calculation of Web pages
11 Sep 2014-pp 788-791
TL;DR: This research wok presents an improved PageRank algorithm that computes the PageRank values of the Web pages more precisely based on time and location based analysis.
Abstract: Web page pre-fetching techniques are used to address the access latency problem of the Internet. In order to perform successful pre-fetching, a page ranking model is required pre-compute the next set of pages that are likely to be accessed by users. The PageRank algorithm is used to compute the importance of a set of Web pages based on their link structure, number of hit, time and area or location based. Some factors are adopted to personalize PageRank, so that it favors the pages that are more important to users. This research wok presents an improved PageRank algorithm that computes the PageRank values of the Web pages more precisely based on time and location based analysis.
Citations
More filters
01 Jun 2018
TL;DR: Most popular keyword extraction algorithms the TF-IDF, TextRank and RAKE algorithm are implemented; the retrieved keywords are compared with the manually selected keywords to check the effectiveness in finding important keywords from single document.
Abstract: The Automatic Text Summarization is most discussed area in Text Mining; there are various techniques available in text mining for text summarization. The two type of summarization are the extractive and abstractive text summarization. The main aim of text summarization is to obtain the concise meaningful text from the original text document. Keywords plays an important role in building a summarization text, there are several keyword extraction algorithms were proposed. In this paper, we implemented most popular keyword extraction algorithms the TF-IDF(a baseline algorithm), TextRank and RAKE algorithm. These keywords extraction algorithms were tested their effectiveness in finding important keywords from single document; the retrieved keywords are compared with the manually selected keywords. The comparison is performed to check the performance of each implemented algorithms with each other and with manually selected keywords.
3 citations
Cites background from "Time and location based summarized ..."
...Usually the traditional, standard statistical evaluation matrices are used those are precision, recall and F-measure[10]....
[...]
...They used traditional evolution matrices precision, recall and F-measure, calculated the Jaccard’s similarity co-efficient between the manual keyword extraction and obtained result from set of data to know the similarity between them....
[...]
...By considering the precision, recall and F-measure value the proposed method of TextRank exploiting on Wikipedia shows better result in comparison with the traditional TextRank, and baseline algorithm TF-IDF for keyword extraction from dataset....
[...]
...As in the F-measure evaluation the TF-IDF outperforms than RAKE and TextRank....
[...]
...Usually the traditional, standard statistical evaluation matrices are used those are precision, recall and F-measure[10]....
[...]
References
More filters
Journal Article•
TL;DR: Google as discussed by the authors is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.
13,327 citations
25 Mar 2002
TL;DR: This work presents an improved PageRank algorithm that computes the PageRank values of the Web pages correctly and works out well in any situations, and the sum of all PageRankvalues is always maintained to be one.
Abstract: The Google search site (http://www.google.com) exploits the link structure of the Web to measure the relative importance of Web pages. The ranking method implemented in Google is called PageRank [3]. The sum of all PageRank values should be one. However, we notice that the sum becomes less than one in some cases. We present an improved PageRank algorithm that computes the PageRank values of the Web pages correctly. Our algorithm works out well in any situations, and the sum of all PageRank values is always maintained to be one. We also present implementation issues of the improved algorithm. Experimental evaluation is carried out and the results are also discussed.
59 citations
"Time and location based summarized ..." refers background in this paper
...Calculation of pagerank [1] is most important topics for any web search engine....
[...]
11 Apr 2013
TL;DR: A new page ranking algorithm is proposed named Enhanced-RatioRank in which the inlinks, outlinks and the number of times user visits the link of the webpages are considered.
Abstract: Due to the dynamic nature of the web plenty of webpages are deleted and added newly and as the web is large collection of data in form of the webpages or in the group of webpages as websites. So every time a surfer searches the web using the search engine, the data should be fresh and relevant. Due to the large size of web, corresponding to any query made by the user number of pages are being retrieved so the result should be ordered in the manner that most relevant webpage is at the top of the list, for which number of mathematical algorithms are used. In this paper a few of link based page ranking algorithm are revised and a new page ranking algorithm is proposed named Enhanced-RatioRank in which the inlinks, outlinks and the number of times user visits the link of the webpages. The relevancy of the webpages returned is more because the user behavior is also considered to rank the webpages.
12 citations
"Time and location based summarized ..." refers background in this paper
...Inbound and outbound links [3] of the webpages are considered to be the primary issue of calculating the pageranks....
[...]
...[3] proposes a new page ranking algorithm named Enhanced-RatioRank in which the inlinks, outlinks and the number of times user visits the link of the webpages are considered....
[...]
12 Jul 2011
TL;DR: A modified random surfer model is proposed, which could lead to a more predictable time for computing PageRank, one of the theories that are studied over the years by researchers.
Abstract: PageRank is an approach to evaluate the importance of a web page implemented by Google. It is one of the important system features that Google usedin order to improve the quality of search result in the original version of Google apart from utilizing link (anchor) in web pages. The success of Google has led to various researches on the theory behind Google search. PageRank is one of the theories that arestudied over the years by researchers. Various theories are proposed to enhance PageRank in terms of its quality andcomputation time. This paper explains the behavior of Markov chain involved in a random surfer model from the original PageRank. A modified random surfer model is proposed, which could lead to a more predictable time for computing PageRank.
11 citations
"Time and location based summarized ..." refers background or methods in this paper
...[7] explains the behaviour of markov chain involved in a random surfer model from the original pagerank....
[...]
...Markov chain [7] based analysis is also performed on calculating pagerank....
[...]
01 Oct 2007
TL;DR: The auto text summarization method based on multi-source integration is introduced, and the full text of each web page is replaced by its auto-generated abstract to compute the relevance between the webpage and user query.
Abstract: Though link analysis based page ranking approaches have reached great success in commercial search engines (SE), the content based relevance computing approaches also play a very important role in the ranking of information retrieval results. Since most of existing relevance computing algorithms are running on the full text of a web page, this paper is focused on the relevance computing between user's query and the auto-generated text summarization of each webpage. The first part of this paper provides a brief introduction of the state of art of relevance computing in SE. The inference network approach is especially concerned in this paper since it is the baseline method in our experiment SE system. Then the auto text summarization method based on multi-source integration is introduced, and the full text of each web page is replaced by its auto-generated abstract to compute the relevance between the webpage and user query. To evaluate the effect of the condensation representation of full text on the relevance based page rank of a system, several experiments are conducted in the last part of this paper, which include the method remarked above with different compress ratio, and the full text based ranking. In addition to the efficiency gain of the SE system, the experiment results also shows that the ranking results based on the summary generated by our text summarization system with 30% compress ratio can also get 11.29% of the precision improvement for the SE system.
7 citations
"Time and location based summarized ..." refers background in this paper
...Content based relevance computing [4] approaches also play a very important role in the ranking of information retrieval results....
[...]