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In a document retrieval system where data is stored and compared with a specific query and then compared with other documents, we need to find the document that is most similar to the query. The most similar document will have the weight higher than other documents. When more than one document are proposed to the user, these documents have to be sorted according to their weights. Once the result is presented to the user by a recommender system, the user may check any document of interest. If there are two different documents' lists, as two proposed results presented by different recommender systems, then, there is a need to find which list is more efficient. To do so, the measuring tool "Search Engine Ranking Efficiency Evaluation Tool [SEREET]" came to existence. This tool assesses the efficiency of each documents list and assigns a numerical value to the list. The value will be closer to 100% if the ranking list efficiency is high which means more relevance documents exist in the list and documents are sorted according to their relevance to the user. The value will be closer to 0% when the ranking list efficiency is poor and all of the presented documents are uninteresting documents to the user. A model to evaluate ranking efficiency is proposed in the dissertation, then it is proved it mathematically. Many mechanisms of search engine have been proposed in order to assess the relevance of a web page. They have focused on keyword frequency, page usage, link analysis and various combinations of them. These methods have been tested and used to provide the user with the most interesting web pages, according to his or her preferences. The collaborative filtering is a new approach, which was developed in this dissertation to retrieve the most interesting documents to the user according to his or her interests. Building a user profile is a very important issue in finding the user interest and categorizes each user in a suitable category. This is a requirement in collaborative filtering implementation. The inference tools such as time spent in a web page, mouse movement, page scrolling, mouse clicks and other tools were investigated. Then the dissertation shows that the most efficient and sufficient tool is the time a user spent on a web page. To eliminate errors, the system introduces a low threshold and high threshold for each user. Once the time spent on a web page breaks this threshold, an error is reported. SEREET tool is one of the contributions to the scientific society, which measures the efficiency of a search engine ranking list. Considerable work were carried, then the conclusion was that the amount of time spent on a web page is the most important factor in determining a user interest of a web page and also it is a sufficient tool which does not require collaborations from other tools such as mouse movements or a page scrolling. The results show that implicit rating is a satisfactory measure and can replace explicit rating. New filtering technique was introduced to design a fully functional recommender system. The linear vector algorithm which was introduced improves the vector space algorithm (VSA) in time complexity and efficiency. The use of machine learning enhances the retrieved list efficiency. Machine learning algorithm uses positive and negative examples for the training, these examples are mandatory to improve the error rate of the system. The result shows that the amount of these examples increases proportionally with the error rate of the system.

The expanded internet usage has resulted in an increased activity at web-based search engines. Companies are therefore devoting a large portion of their online marketing budget on Search Engine Marketing (abbreviated SEM) in order to reach potential online consumers searching for products. SEM comprises Search Engine Advertising (SEA) and Search Engine Optimization (SEO) which are two dissimilar marketing tools companies can invest in to reach the desired customer segments. It is therefore of great interest for companies in different product markets to have knowledge of which SEM strategy to utilize. The statement leads to the purpose of the thesis which is to investigate which SEM strategy is the most suitable for companies in different markets, SEA or SEO?. The purpose of the thesis is derived to the research problem: How does the search behavior of consumers differ between the two SEM tools, SEO and SEA?. Initially, in order to answer the research problem, a theoretical framework was conducted consisting of theories from previous research. To collect primary data observations of 60 test subjects was performed in accordance with the Experimental Vignette Methodology. The analysis consists of a comparison between the collected data and the theories included in the frame of reference, to identify similarities and differences. The SPSS analysis of the result revealed numerous findings such as the two-way interactions of the factors degree of involvement and the click rate of SEM, as well as the choice of either a head or a tail keyword and the degree of involvement. The analysis further revealed a three-way interaction which suggests that the degree of involvement, and the use of either a head or tail keyword affects the choice of SEM. Additionally, the result shows that customers using brands as keywords are more likely to click on an organic link rather than on a paid ad. However, when adding the factor age to the analysis the results turn insignificant. As the area of search behavior of customers using search engines is relatively scientifically unexplored, the thesis has contributed with knowledge useful for companies, marketing agencies, among others. However, due to the ongoing expansion of search engine usage, it is of great interest to conduct further research in the area to reveal additional findings.

Aim: The aim of this study is to analyze the usage of Search Engine Optimization and Knowledge Graphs and the connection between them to achieve profitable business visibility and reach. Methods: Following a qualitative method together with an inductive approach, ten marketing professionals were interviewed via an online questionnaire. To conduct this study both primary and secondary data was utilized. Scientific theory together with empirical findings were linked and discussed in the analysis chapter. Findings: This study establishes current Search Engine Optimization utilization by businesses regarding common techniques and methods. We demonstrate their effectiveness on the Google Knowledge Graph, Google My Business and resulting positive business impact for increased visibility and reach. Difficulties remain in accurate tracking procedures to analyze quantifiable results. Contribution of the thesis: This study contributes to the literature of both Search Engine Optimization and Knowledge Graphs by providing a new perspective on how these subjects have been utilized in modern marketing. In addition, this study provides an understanding of the benefits of SEO utilization on Knowledge Graphs. Suggestions for further research: We suggest more extensive investigation on the elements and utilization of Knowledge Graphs; how the structure can be affected; which techniques are most effective on a bigger scale and how effectively the benefits can be measured. Key Words: Search Engine, Search Engine Optimization, SEO, Knowledge Graphs, Google My Business, Google Search Engine, Online Marketing.

Users of current web search engines are often presented with a large number of returns after submitting a search term and choosing from the list might lead to them suffering from the effect of “choice overload”, as reported in earlier work. However, these search results are typically presented in an ordered list so as to simplify the search process, which may influence search behaviour and moderate the effect of number of choices. In this thesis, the effects of the number of search returns and their ordering on user behaviour and satisfaction are explored. A mixed methods approach combining multiple data collection and analysis techniques is employed in order to investigate these effects in terms of three specific issues, namely, choice overload in search engine use, variety seeking behaviour in a situation where multiple aspects of search results are required, and the chance of encountering serendipity. The participants were given search tasks and asked to choose from the sets of returns under experimental conditions. The results from the first three experiments revealed that large numbers of search results returned from a search engine tended to be associated with more satisfaction with the selected options when the decision was made without a time limit. In addition, when time was more strongly constrained the choices from a small number of returns led to relatively higher satisfaction than for a large number. Moreover, users’ behaviour was strongly influenced by the ordering of options in that they often looked and selected options presented near the top of the result lists when they perceived the ranking was reliable. The next experiment further investigated the ranking reliance behaviour when potentially useful search results were presented in supplementary lists. The findings showed that when users required a variety of options, they relied less on the ordering and tended to adapt their search strategies to seek variety by browsing more returns through the list, selecting options located further down, and/or choosing the supplementary web pages provided. Finally, with the aim of illustrating how chance encountering can be supported, a model of an automated synonym-enhanced search was developed and employed in a real-world literature search. The results showed that the synonym search was occasionally useful for providing a variety of search results, which in turn increased users’ opportunity to come across serendipitous experiences.

<p>Information retrieval has been a prevasive part of human society since its existence.With the advent of internet and World wide Web it became an extensive area of researchand major foucs, which lead to development of various search engines to locate the de-sired information, mostly for globally connected computer networks viz. internet.Butthere is another major part of computer network viz. intranet, which has not seen muchof advancement in information retrieval approaches, in spite of being a major source ofinformation within a large number of organizations.Most common technique for intranet based search engines is still mere database-centric. Thus practically intranets are unable to avail the benefits of sophisticated tech-niques that have been developed for internet based search engines without exposing thedata to commercial search engines.In this Master level thesis we propose a ”state of the art architecture” for an advancedsearch engine for intranet which is capable of dealing with continuously growing sizeof intranets knowledge base. This search engine employs lexical processing of doc-umetns,where documents are indexed and searched based on standalone terms or key-words, along with the semantic processing of the documents where the context of thewords and the relationship among them is given more importance.Combining lexical and semantic processing of the documents give an effective ap-proach to handle navigational queries along with research queries, opposite to the modernsearch engines which either uses lexical processing or semantic processing (or one as themajor) of the documents. We give equal importance to both the approaches in our design,considering best of the both world.This work also takes into account various widely acclaimed concepts like inferencerules, ontologies and active feedback from the user community to continuously enhanceand improve the quality of search results along with the possibility to infer and deducenew knowledge from the existing one, while preparing for the advent of semantic web.</p>

Queries submitted to search engines rarely provide a complete and precise description of a user's information need. Most queries are ambiguous to some extent, having multiple interpretations. For example, the seemingly unambiguous query ``tennis lessons'' might be submitted by a user interested in attending classes in her neighborhood, seeking lessons for her child, looking for online videos lessons, or planning to start a business teaching tennis. Search engines face the challenging task of satisfying different groups of users having diverse information needs associated with a given query. One solution is to optimize ranking functions to satisfy diverse sets of information needs. Unfortunately, existing evaluation frameworks do not support such optimization. Instead, ranking functions are rewarded for satisfying the most likely intent associated with a given query. In this thesis, we propose a framework and associated evaluation metrics that are capable of optimizing ranking functions to satisfy diverse information needs. Our proposed measures explicitly reward those ranking functions capable of presenting the user with information that is novel with respect to previously viewed documents. Our measures reflects quality of a ranking function by taking into account its ability to satisfy diverse users submitting a query. Moreover, the task of identifying and establishing test frameworks to compare ranking functions on a web-scale can be tedious. One reason for this problem is the dynamic nature of the web, where documents are constantly added and updated, making it necessary for search engine developers to seek additional human assessments. Along with issues of novelty and diversity, we explore one approximate approach to compare different ranking functions by overcoming the problem of lacking complete human assessments. We demonstrate that our approach is capable of accurately sorting ranking functions based on their capability of satisfying diverse users, even in the face of incomplete human assessments.

Information retrieval has been a prevasive part of human society since its existence.With the advent of internet and World wide Web it became an extensive area of researchand major foucs, which lead to development of various search engines to locate the de-sired information, mostly for globally connected computer networks viz. internet.Butthere is another major part of computer network viz. intranet, which has not seen muchof advancement in information retrieval approaches, in spite of being a major source ofinformation within a large number of organizations.Most common technique for intranet based search engines is still mere database-centric. Thus practically intranets are unable to avail the benefits of sophisticated tech-niques that have been developed for internet based search engines without exposing thedata to commercial search engines.In this Master level thesis we propose a ”state of the art architecture” for an advancedsearch engine for intranet which is capable of dealing with continuously growing sizeof intranets knowledge base. This search engine employs lexical processing of doc-umetns,where documents are indexed and searched based on standalone terms or key-words, along with the semantic processing of the documents where the context of thewords and the relationship among them is given more importance.Combining lexical and semantic processing of the documents give an effective ap-proach to handle navigational queries along with research queries, opposite to the modernsearch engines which either uses lexical processing or semantic processing (or one as themajor) of the documents. We give equal importance to both the approaches in our design,considering best of the both world.This work also takes into account various widely acclaimed concepts like inferencerules, ontologies and active feedback from the user community to continuously enhanceand improve the quality of search results along with the possibility to infer and deducenew knowledge from the existing one, while preparing for the advent of semantic web.

Since the search engine was first released in 1993, the development has never been slow down and various search engines emerged to vied for popularity. However, current traditional search engines like Google and Yahoo! are based on key words which lead to results impreciseness and information redundancy. A new search engine with semantic analysis can be the alternate solution in the future. It is more intelligent and informative, and provides better interaction with users.        This thesis discusses the detail on semantic search, explains advantages of semantic search over other key-word-based search and introduces how to integrate semantic analysis with common search engines. At the end of this thesis, there is an example of implementation of a simple semantic search engine.

Internet has become the most extensible media of data exchange and communication in the world because computer science and technology are more and more popular. The Geographic Information Systems¡]GIS¡^are also developed on the Internet. However, using existing mechanism of data searching on the Internet cannot search data in Web GIS. We can only browse data but not access. This situation makes Web GIS as an isolated island. Users fail to know where and what kinds of data are provided, and these data also cannot be shared. The most important objective of the research is to build an effective mechanism of searching and clearinghouse on the Internet. This mechanism can help computer overcome difficulties in reading and understanding geographic data that are composed of maps and images, and then geographic data can be searched and shared easily as text data. The research will try to create metadata by XML that are complied with FGDC standard. By using two of the XML characteristics, i.e. creating tags and describing data, the computer can retrieve information automatically from metadata on the Internet. Lastly, the geographic search engine and clearinghouse that the research built will collect and integrate geographic metadata to systematically facilitate users finding geographic data they need through Internet, and achieve the objectives of geographic data search and clearinghouse on the Internet.