Search Engines (SEs) have become the most significant tool for retrieving information from World Wide Web (WWW) against user-generated queries. Presently, data available on Internet is growing exponentially in three different dimensions i.e. size, processing power, & software intelligence. The WWW has evolved being a collection of static content (web 1.0) to intelligent web (Web 3.0). To re...

When a query is submitted to a search engine, the search engine returns a dynamically generated result page that contains the number of hits (i.e., the number of matching results) for the query. Hit number is a very useful piece of information in many important applications such as obtaining document frequencies of terms, estimating the sizes of search engines and generating search engine summa...

With rapid growth of information on the Web, Web search engines have gained great momentum for exploiting valuable Web resources. Although keywords-based Web search engines provide relevant search results in response to users’ queries, future enhancement is still needed. Three important issues include (1) search results can be diverse because ambiguous keywords in queries can be interpreted to ...

With the explosive growth of information in the Web, users face difficulties while finding their desired information. Search engine helps the user by retrieving useful information from this huge collection based on his/her search query and presents a list of relevant web pages as a search result. However, without proper ranking of pages in the result through the relevancy of pages to the search...

Annually, web search engine providers spend more and more money on documents ranking in search engines result pages (SERP). Click models provide advantageous information for ranking documents in SERPs through modeling interactions among users and search engines. Here, three modules are employed to create a hybrid click model; the first module is a PGM-based click model, the second module in a d...

This paper evaluates the effectiveness of some “unofficial” factors in Search Engine Optimisation. A summary of official Google guidelines is given followed by a review of “unofficial” ranking factors as reported by a number of experts in the field of Search Engine Optimisation”. These opinions vary and do not always agree. Experiments on keyword density, web page titles and the use of outbound...

User behavior signals such as clicks are strong indicators of a search engines performance. Many existing search algorithms focus on predicting users interactions, by optimizing a relevance cost function for the query and individual web documents. The result set (list) is then generated by ranking web documents with this score. However, the probability of user interaction with a web document on...

Users of web search engines are known to mostly focus on the top ranked results of the search engine result page. While many studies support this well known information seeking pattern only few studies concentrate on the question what users are missing by neglecting lower ranked results. To learn more about the relevance distributions in the so-called long tail we conducted a relevance assessme...

Automated search engines continuously discover, index, and store information about web pages. When a user issues a query, this repository is searched to find a result set of most relevant pages. An ideal search scheme must satisfy two basic requirements: high recall, and high precision. Recall measures the ability of an algorithm to find as many relevant pages as possible. Precision measures th...