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quality of information retrieval. The contributions of our research are twofold. First, the existing ranking algorithms of search engine are classified. And we extend expression of queries by “keyword and ”, instead of keywords only. Second, a new ranking algorithm based on user feedback and semantic tags is
This paper presents an attempt to show the efficiency of some search engines in dealing with Arabic keywords. This can be achieved by comparing the number of retrieved pages, retrieving time, and stability (in both the number of retrieved pages and the order for each retrieved page) for each one of the selected 20
focused web crawler under the EU FP7 Security Research Project CAPER (Collaborative information, Acquisition, Processing, Exploitation and Reporting for the prevention of organized crime). The crawler allows 1. to look for documents starting from a URL until a parametric depth of levels - also specifying a keyword that has
Search engine optimization (SEO) is a process of improving the prominence of a website. Following a reverse engineering approach, in this paper, we study and analyze the key influence factors in the process of web search. We firstly build a system to automatically crawl all factors of 200 thousand web pages. Then we make a content analysis including Page Rank, URL and HTML analysis based on top 20...
The Web represents one of the largest repositories of information ever compiled by mankind and as such search techniques are essential to navigating its depths and returning pertinent information. Typically the search techniques employed in search engines such as Google entail the use of keywords in which Web pages
The newly proposed Alert Notification Service (ANS) represents a web service that automatically visits all requested web sites selected by a certain user and alerts the user when a certain keyword phrase has been changed. This saves users' time and effort by reducing the repeatedly visiting multiple web sites looking
In order to solve the problem that we can only collect data from one single data source at some fixed time after mining the keywords in a rather superficial level, and to take full use of the information returned by search engines to construct the social relationship network based on the semantic link of the searched
For most of the current search engines, the difference of their returned results are only because of the different keywords, i.e. for the same keywords used for searching, the same results will be returned. In fact, different users may have different search purposes even if they use the same keywords. In this paper
improving user's goals and needs. The content based ranking is based on contents and keywords rather than link structure and keywords provided by search engines. Search engines results are retrieved based on the user query. Also, usage based ranking algorithm consider the past user navigation pattern and analyze the behavior
some keywords, the search engine will return all the web pages related to the keywords in a reasonable order with some sort algorithm. In this process, retrieval results sort algorithm plays a significant role of user satisfaction. This paper starts from the background of sort algorithm, and introduces the history and
In order to study the retrieval precision of network information and solve the problems existed in search engines this paper analyzes problems and precision in the information retrieval according to experimental data such as image retrieval with different retrieval keywords, and puts forward a new construction of
option, say, limiting search to few links. To reduce the time spent by users, a web link extraction tool has been designed and implemented in Java, that analyzes the ways of extracting web link information using a standard interface. The Test Scenario has been presented with various keywords like Higher Education
blogs, translates into English, categorizes the blog based on the content by keywords and provides code for posting relevant ads in blog. The system is tested on 100 regional language blogs in various categories and found to be effective.
It is well known that the key issue of online marketing is to accurately find the target user groups for the corresponding advertisements. Traditionally, the advertising products target user groups based on search keywords (e.g. AdWords), page visiting (e.g. AdSense), and etc. In this work, we explore a new targeting
keywords, the number of communities, the average clustering coefficient, and the average similarities of web pages. These five impact factors contain statistic and content information of an event. Empirical experiments on real datasets including Google Zeitgeist and Google Trends show that that the number of web pages and the
to the purchase page of the product or the service being promoted. The paper proposes a method that suggests the keywords of a web page based on the frequent terms in a web page while including the lexical relationship (synonyms) of these words. An experiment is executed to validate the method while the method's result
recursive partitioning ranking scheme, are capable of reverse engineering Google's ranking algorithm with high accuracy. As an example, we manage to correctly predict 7 out of the top 10 pages for 78% of evaluated keywords. Moreover, for content-only ranking, our system can correctly predict 9 or more pages out of the top 10
As various individuals and organizations disseminate information on their Web pages, real-world social events and changes are considered to be reflected in Web trends. The billions of Web pages that now exist are retrieved by Web search engines which accept keywords and return a search engine results page (SERP
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