<p>This paper is a literature study on search engine optimization (SEO) considering the leader of the search engine market: Google. There´s an introductory background description of Google and its methods of  crawling the Internet and indexing the web pages, along with a brief review of the famous PageRank algorithm. The purpose of this paper is to describe the major important methods for improved rankings on Google´s result lists. These methods could be categorized as on-page methods tied to the website to be optimized or off-page methods that are external to the website such as link development. Furthermore the most common unethical methods are described, known as “black hat”, which is the secondary purpose of the text. The discussion that follows concerns the practical implications of SEO and personal reflections of the matter. Finally  there´s a quick view of the expanding market of handheld devices connected to the Internet and mobile search as an initial area of research.</p> / <p>Denna uppsats är en litteraturstudie om ämnet sökmotoroptimering (SEO) som behandlar ledaren bland sökmotorer: Google. Det finns en introducerande bakgrund som beskriver Google och dess metoder för ”crawling” och indexering av webbplatser, tillsammans med en kort genomgång av den  välkända  PageRank-algoritmen. Syftet med denna uppsats är att beskriva de centrala metoderna för förbättrad rankning i Googles träffresultat. Dessa metoder kan kategoriseras som ”on-page”-metoder, som är knutna till webbplatsen, eller ”off-page”-metoder, som är externa, exempelvis  länkförvärvning. Vidare kommer de vanligaste oetiska metoderna att beskrivas, kända som ”black hat”, som är det andra syftet med denna text. Diskussionen som följer behandlar de praktiska implikationerna av SEO och personliga reflektioner i frågan. Avslutningsvis  berör vi  den expanderade marknaden av handhållen utrustning med Internetuppkoppling och mobil sökning som ett kommande forskningsområde.</p>

This paper is a literature study on search engine optimization (SEO) considering the leader of the search engine market: Google. There´s an introductory background description of Google and its methods of  crawling the Internet and indexing the web pages, along with a brief review of the famous PageRank algorithm. The purpose of this paper is to describe the major important methods for improved rankings on Google´s result lists. These methods could be categorized as on-page methods tied to the website to be optimized or off-page methods that are external to the website such as link development. Furthermore the most common unethical methods are described, known as “black hat”, which is the secondary purpose of the text. The discussion that follows concerns the practical implications of SEO and personal reflections of the matter. Finally  there´s a quick view of the expanding market of handheld devices connected to the Internet and mobile search as an initial area of research. / Denna uppsats är en litteraturstudie om ämnet sökmotoroptimering (SEO) som behandlar ledaren bland sökmotorer: Google. Det finns en introducerande bakgrund som beskriver Google och dess metoder för ”crawling” och indexering av webbplatser, tillsammans med en kort genomgång av den  välkända  PageRank-algoritmen. Syftet med denna uppsats är att beskriva de centrala metoderna för förbättrad rankning i Googles träffresultat. Dessa metoder kan kategoriseras som ”on-page”-metoder, som är knutna till webbplatsen, eller ”off-page”-metoder, som är externa, exempelvis  länkförvärvning. Vidare kommer de vanligaste oetiska metoderna att beskrivas, kända som ”black hat”, som är det andra syftet med denna text. Diskussionen som följer behandlar de praktiska implikationerna av SEO och personliga reflektioner i frågan. Avslutningsvis  berör vi  den expanderade marknaden av handhållen utrustning med Internetuppkoppling och mobil sökning som ett kommande forskningsområde.

Le classement des objets est une des questions importantes et typiques dans notre vie quotidienne. De nombreuses applications ont besoin de classifier des objets en fonction de certains critères, parfois simple comme de classifier les étudiants dans une classe en fonction de relevé de notes ou plus compliqué comme le classement des universités. Classifier des objets consiste à les ordonner selon certains critères exigés par une application spécifique.Avec la popularisation de l’Internet, un problème typique qui a émergé des deux dernières décennies est le classement des résultats renvoyés par les moteurs de recherche. Dans les moteurs de recherche classiques (comme Google, Yahoo ou Bing ),l’importance d’une page web est la base pour le classement. Cette valeur est calculée sur la base de l’analyse des hyper-liens entre les pages Web. Avec un ensemble de documents V={v1, ..., vn}, quand il y a une requête q d’un utilisateur arrivant, le moteur de recherche cherche des documents dans V correspondant à la requête q, puis trie les documents dans l’ordre décroissant de leur pertinence pour la requête. Ce processus peut être réalisé grâce à une fonction de classement qui permet de cal culer la similarité sim(q, vi) entre la requête q et un document vi ∈ V. La fonction de classement peut être considérée comme le noyau qui détermine essentiellement la qualité du moteur de recherche. / Ranking objects is one of the important and typical issues in our daily life. Many applications need to rank objects according to certain criteria, as simple as ranking students in a class according to average grades, or more complicated as ranking universities. Ranking objects means to arrange them in accordance with some criteria depending on the specific application.In the era of the Internet, a typical problem emerging in the last decades is the ranking of results returned by search engines. In conventional search engines (like Google, Yahoo or Bing ), the importance of a web page is the basis for ranking. This value is determined based on the analysis of graph links between web pages. With a set of documents V={v1, ..., vn}, when there is a user’s query q arriving, the search engine looks for documents in V matching the query q, then sorts the documents according to their relevance to the query in descending order. This process can be done thanks to a ranking function which allows us to compute the similarity s(q,vi) between the query q and a document vi ∈ V . Obviously, the ranking function can be seen as the core and significantly determines the quality of the search engine.

Keyphrases describe a document in a coherent and simple way, giving the prospective reader a way to quickly determine whether the document satisfies their information needs. The pervasion of huge amount of information on Web, with only a small amount of documents have keyphrases extracted, there is a definite need to discover automatic keyphrase extraction systems. Typically, a document written by human develops around one or more general concepts or sub-concepts. These concepts or sub-concepts should be structured and semantically related with each other, so that they can form the meaningful representation of a document. Considering the fact, the phrases or concepts in a document are related to each other, a new approach for keyphrase extraction is introduced that exploits the semantic relations in the document. For measuring the semantic relations between concepts or sub-concepts in the document, I present a comprehensive study aimed at using collaboratively constructed semantic resources like Wikipedia and its link structure. In particular, I introduce a graph-based keyphrase extraction system that exploits the semantic relations in the document and features such as term frequency. I evaluated the proposed system using novel measures and the results obtained compare favorably with previously published results on established benchmarks.

<p> Personalized PageRank with high teleportation probability enables exploring the environment of a seed. With this insight, one can use an orthogonal factorization of a set of personalized PageRank vectors, like SVD, to derive a 2-dimensional representation of the network. This can be done for the whole network or a smaller piece. The power of this method lies in the fact that only a few columns, compared to the size of the networks, can be used to generate a local representation of the part of the network we are interested in. This technique has the potential to be seamlessly used for higher order structures, such as hypergraphs which have found a great deal of use for real-world data. This work investigates the characteristics of personalized PageRank and how it compares to the transition probabilities on the graph in terms of their ability to develop low dimensional representations. A key focus of the thesis are the similarities between the embeddings generated due to PageRank and those generated by spectral methods.</p>

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Master of Science / Department of Computing and Information Science / Daniel Andresen / With massive explosion of data in recent times and people depending more and more on search engines to get all kinds of information they want, it has becoming increasingly difficult for the search engines to produce most relevant data to the users. PageRank is one algorithm that has revolutionized the way search engines work. It was developed by Google`s Larry Page and Sergey Brin. It was developed by Google to rank websites and display them in order of ranking in its search engine results. PageRank is a link analysis algorithm that assigns a weight to each document in a corpus and measures the relative importance within the corpus. The purpose of my project is to extract all the English Wikipedia data using MediaWiki API and JWPL(Java Wikipedia Library), build PageRank Algorithm and analyze its performance on the this data set. Since the data set is too big to run in a single node Hadoop cluster, the analysis is done in a high computation cluster called Beocat, provided by Kansas State University, Computing and Information Sciences Department.

Living in the information-overloading age, many people find it difficult to assimilate the information and to identify resources they need. As to a consumer, browsing, searching, and buying a product on online stores is often a time-consuming and frustrating task with the flourishing development of e-commerce. Many shoppers who are interested in buying products on E-commerce websites end up finding nothing they want. Therefore, many E-commerce websites have implemented recommender systems that intend to provide consumers with personalized recommendations for various types of products and services. Some recent research has taken into account social influence in recommender systems in E-commerce. These recommender systems have been observed to achieve better accuracy of prediction, and have also overcome some of the problems of the previous methods. In this study, we propose a trust network-based recommendation framework that utilizes the trust relationship between users to generate recommendation. We employ PageRank algorithm for trust matrix adjustment and recommendation. In addition, we propose several assumptions that can be used to construct trust matrix, and we verify them by experiments. We finally identify two approaches for adjusting trust matrix. Bases on the trust and rating data collected from Epinion.com, we exercise several alternatives and evaluated many combinations of trust matrix adjustment and recommendation methods. Our experiment evaluation results show that using different pagerank for different users groups can generate better recommendation results. Moreover, we proposed a best hybrid method that achieves the best performance.

The thesis first reviews the mathematics behind the Google’s PageRank, which is the state-of-the-art webpage ranking algorithm. The main focus of the thesis is on exploring a lazy PageRank and variants, related to a random walk, and by realizing that, they can be computed using the very same algorithm, find lazy PageRank and variants' expressions for some common graph structures, for example, a line-graph, a complete-graph, a complete-bipartite graph including a star graph, and try to get some understanding of the behavior of the PageRank, when a network evolves, for example either by a contraction or an expansion of graphs’ nodes or links.

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