How to Promote Community Portals

2011 ◽  
pp. 454-460
Author(s):  
Aki Vainio ◽  
Kimmo Salmenjoki
Keyword(s):  
Information Content ◽  
Search Engines ◽  
Web Pages ◽  
Web Environment ◽  
The Common ◽  
Marketing Tool ◽  
The Masses ◽  
The Web

Information content of the Web has, in the last 10 years, changed from informative to communicative. Web pages, especially homepages, were the foremost places where companies, organizations, and individuals alike expressed their existence online and provided some information about themselves, like their products, services, or artefacts that they related to. On the common Web environment, the search engines were harvesting this information and made it available and meaningful for the masses of Web users. In the early days of Web, this factor alone justified the usage of Web as a marketing tool and as an easy way to share important information between collaborating partners.

Critical Analysis of Major Search Engines

2019 ◽  
Vol 16 (9) ◽  
pp. 3712-3716
Author(s):  
Kailash Kumar ◽  
Abdulaziz Al-Besher
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Critical Analysis ◽  
Research Paper ◽  
Web Pages ◽  
Ranking Algorithm ◽  
Sponsored Search ◽  
Web Page ◽  
Rigorous Analysis ◽  
The Common

This paper examines the overlapping of the results retrieved between three major search engines namely Google, Yahoo and Bing. A rigorous analysis of overlap among these search engines was conducted on 100 random queries. The overlap of first ten web page results, i.e., hundred results from each search engine and only non-sponsored results from these above major search engines were taken into consideration. Search engines have their own frequency of updates and ranking of results based on their relevance. Moreover, sponsored search advertisers are different for different search engines. Single search engine cannot index all Web pages. In this research paper, the overlapping analysis of the results were carried out between October 1, 2018 to October 31, 2018 among these major search engines namely, Google, Yahoo and Bing. A framework is built in Java to analyze the overlap among these search engines. This framework eliminates the common results and merges them in a unified list. It also uses the ranking algorithm to re-rank the search engine results and displays it back to the user.

scholarly journals Distributed and collaborative Web Change Detection system

2015 ◽  
Vol 12 (1) ◽  
pp. 91-114 ◽  
Author(s):  
Víctor Prieto ◽  
Manuel Álvarez ◽  
Víctor Carneiro ◽  
Fidel Cacheda
Keyword(s):  
Change Detection ◽  
Search Engines ◽  
Web Site ◽  
Detection System ◽  
Computational Cost ◽  
Web Pages ◽  
Web Page ◽  
Case Scenario ◽  
Worst Case ◽  
The Web

Search engines use crawlers to traverse the Web in order to download web pages and build their indexes. Maintaining these indexes up-to-date is an essential task to ensure the quality of search results. However, changes in web pages are unpredictable. Identifying the moment when a web page changes as soon as possible and with minimal computational cost is a major challenge. In this article we present the Web Change Detection system that, in a best case scenario, is capable to detect, almost in real time, when a web page changes. In a worst case scenario, it will require, on average, 12 minutes to detect a change on a low PageRank web site and about one minute on a web site with high PageRank. Meanwhile, current search engines require more than a day, on average, to detect a modification in a web page (in both cases).

scholarly journals The Canon of Dutch Literature According to Google

2019 ◽  
Author(s):  
Lucas van der Deijl ◽  
Antal van den Bosch ◽  
Roel Smeets
Keyword(s):  
Knowledge Base ◽  
Search Engines ◽  
Literary History ◽  
Information Sources ◽  
Search Algorithms ◽  
Web Pages ◽  
Printed Media ◽  
Literary Histories ◽  
Dutch Literature ◽  
The Web

Literary history is no longer written in books alone. As literary reception thrives in blogs, Wikipedia entries, Amazon reviews, and Goodreads pro les, the Web has become a key platform for the exchange of information on literature. Al- though conventional printed media in the eld—academic monographs, literary supplements, and magazines—may still claim the highest authority, online me- dia presumably provide the rst (and possibly the only) source for many readers casually interested in literary history. Wikipedia o ers quick and free answers to readers’ questions and the range of topics described in its entries dramatically exceeds the volume any printed encyclopedia could possibly cover. While an important share of this expanding knowledge base about literature is produced bottom-up (user based and crowd-sourced), search engines such as Google have become brokers in this online economy of knowledge, organizing information on the Web for its users. Similar to the printed literary histories, search engines prioritize certain information sources over others when ranking and sorting Web pages; as such, their search algorithms create hierarchies of books, authors, and periods.

Clustering Users According to Common Interest Based on User Search Behavior

2010 ◽  
Vol 143-144 ◽  
pp. 851-855 ◽  
Author(s):  
Pei Ying Zhang ◽  
Ya Jun Du ◽  
Chang Wang
Keyword(s):  
Recommendation System ◽  
Search Behavior ◽  
Common Interest ◽  
Web Pages ◽  
User Session ◽  
Novel Method ◽  
The Common ◽  
Syntactic Similarity ◽  
User Search Behavior ◽  
The Web

The paper presents a novel method to cluster users who share the common interest and discover their common interest domain by mining different users’ search behaviors in the user session, mainly the consecutive search behavior and the click sequence considering the click order and the syntactic similarity. The community is generated and this information will be used in the recommendation system in the future. Also the method is ‘content-ignorant’ to avoid the storage and manipulation of a large amount of data when clustering the web pages by content. The experiment proved it an available and effective way.

Analysis of Web Pages Based the Changed Information and its’ Application in the Search Engine for one Web Site

2013 ◽  
Vol 303-306 ◽  
pp. 2311-2316
Author(s):  
Hong Shen Liu ◽  
Peng Fei Wang
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Site ◽  
New Method ◽  
Web Pages ◽  
Web Crawler ◽  
The Core ◽  
Core Technology ◽  
The Web

The structures and contents of researching search engines are presented and the core technology is the analysis technology of web pages. The characteristic of analyzing web pages in one website is studied, relations between the web pages web crawler gained at two times are able to be obtained and the changed information among them are found easily. A new method of analyzing web pages in one website is introduced and the method analyzes web pages with the changed information of web pages. The result of applying the method shows that the new method is effective in the analysis of web pages.

scholarly journals Real and Promoted Aesthetic Properties of Geosites: New Empirical Evidence from SW Russia

Heritage ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 160-170
Author(s):  
Dmitry A. Ruban ◽  
Natalia N. Yashalova
Keyword(s):  
Physical Properties ◽  
Empirical Evidence ◽  
Web Pages ◽  
Significant Discrepancy ◽  
Natural Heritage ◽  
Aesthetic Properties ◽  
Adequate Representation ◽  
The Common ◽  
Practical Recommendations ◽  
The Web

Aesthetic properties of natural heritage objects are determined by their physical properties. Online promotion of these objects to potential tourists requires adequate representation of these properties on web pages. The Shum waterfall is a small, but notable and tourism-important geosite of southwestern Russia. Its real aesthetic properties were examined in the field, and 20 web pages devoted to local tourism were examined to judge its promoted aesthetic properties. Eleven criteria of the common tourists’ judgments of beauty were used for this purpose. A significant discrepancy between the real and promoted properties is found. Particularly, the web pages exaggerate the scale of the waterfall and do not mention crowds of tourists. This may cause disappointment of the latter. The findings of the present study allow for making several practical recommendations for more efficient promotion of the Shum waterfall, as well as providing general advice to the geotourism industry.

scholarly journals Web Crawler and Web Crawler Algorithms: A Perspective

2020 ◽  
Vol 9 (5) ◽  
pp. 203-205
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
The Internet ◽  
Web Pages ◽  
Web Crawler ◽  
Day By Day ◽  
The Web

A web crawler is also called spider. For the intention of web indexing it automatically searches on the WWW. As the W3 is increasing day by day, globally the number of web pages grown massively. To make the search sociable for users, searching engine are mandatory. So to discover the particular data from the WWW search engines are operated. It would be almost challenging for mankind devoid of search engines to find anything from the web unless and until he identifies a particular URL address. A central depository of HTML documents in indexed form is sustained by every search Engine. Every time an operator gives the inquiry, searching is done at the database of indexed web pages. The size of a database of every search engine depends on the existing page on the internet. So to increase the proficiency of search engines, it is permitted to store only the most relevant and significant pages in the database.

Classification of Web Pages Using Machine Learning Techniques

2010 ◽  
pp. 134-150
Author(s):  
K. Selvakuberan ◽  
M. Indra Devi ◽  
R. Rajaram
Keyword(s):  
Machine Learning ◽  
Search Engines ◽  
Research Problem ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Web Pages ◽  
Current Research Problem ◽  
Learning Techniques ◽  
The Web

The explosive growth of the Web makes it a very useful information resource to all types of users. Today, everyone accesses the Internet for various purposes and retrieving the required information within the stipulated time is the major demand from users. Also, the Internet provides millions of Web pages for each and every search term. Getting interesting and required results from the Web becomes very difficult and turning the classification of Web pages into relevant categories is the current research topic. Web page classification is the current research problem that focuses on classifying the documents into different categories, which are used by search engines for producing the result. In this chapter we focus on different machine learning techniques and how Web pages can be classified using these machine learning techniques. The automatic classification of Web pages using machine learning techniques is the most efficient way used by search engines to provide accurate results to the users. Machine learning classifiers may also be trained to preserve the personal details from unauthenticated users and for privacy preserving data mining.

Classification of Web Pages Using Machine Learning Techniques

2012 ◽  
pp. 50-65 ◽  
Author(s):  
K. Selvakuberan ◽  
M. Indra Devi ◽  
R. Rajaram
Keyword(s):  
Machine Learning ◽  
Search Engines ◽  
Research Problem ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Web Pages ◽  
Current Research Problem ◽  
Learning Techniques ◽  
The Web

The explosive growth of the Web makes it a very useful information resource to all types of users. Today, everyone accesses the Internet for various purposes and retrieving the required information within the stipulated time is the major demand from users. Also, the Internet provides millions of Web pages for each and every search term. Getting interesting and required results from the Web becomes very difficult and turning the classification of Web pages into relevant categories is the current research topic. Web page classification is the current research problem that focuses on classifying the documents into different categories, which are used by search engines for producing the result. In this chapter we focus on different machine learning techniques and how Web pages can be classified using these machine learning techniques. The automatic classification of Web pages using machine learning techniques is the most efficient way used by search engines to provide accurate results to the users. Machine learning classifiers may also be trained to preserve the personal details from unauthenticated users and for privacy preserving data mining.

scholarly journals Optimizing performance of search engines based on user behavior

2018 ◽  
Vol 7 (2.7) ◽  
pp. 359
Author(s):  
Dr Jkr Sastry ◽  
M Sri Harsha Vamsi ◽  
R Srinivas ◽  
G Yeshwanth
Keyword(s):  
Search Engines ◽  
User Behavior ◽  
Search Process ◽  
Web Pages ◽  
User Characteristics ◽  
End User ◽  
The Web

WEB clients use the WEB for searching the content that they are looking for through inputting keywords or snippets as input to the search engines. Search Engines follows a process to collect the content and provide the same as output in terms of URL links. One can observe that only 20% of the outputted URLS are of use to the end user. 80% of output is unnecessarily surfed leading to wastage of money and time. Customers have surfing characteristics which can be collected as the user keep surfing. The search process can be made efficient by including the user characteristics / Behaviors as part and parcel of search process. This paper is aimed at improving the search process through integration of the user behavior in indexing and ranking the web pages.

Sign in / Sign up

Export Citation Format

Share Document