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Search Engines that Learn from Their Users

Search Engines that Learn from Their Users

For all these reasons researchers have been looking for alternatives that do not suffer from these drawbacks. The Cranfield way of evaluating can be referred to as offline evaluation because there are no users interacting with a real, online system. In contrast, the alternative then is called online evaluation. Online evaluation uses the interactions of real and unsuspecting users of search engines for evaluation instead of professional human judges. These users have their natural information needs that made them decide to use the search engine. These users have a task in mind that they wish to solve. This task often extends beyond just search [114]. Search, for these users, is just a means to an end. These users come to the search engine, translate their information need into a keyword query and are determined to find what they need to complete their task. To this end they interact with the search engine. They may, for instance, reformulate their query or click around in result lists presented to them. All these traces of user interactions with the search engine are easily captured and can then be interpreted as implicit signals for evaluation. For instance, one could measure how often users need to reformulate their queries before they stop search, and presumably are satisfied with the results. Or one could measure where in a result list users click, the intuition being that it is better if users click higher in such a list because a user scanning the list from top bottom found it faster. Alternatively, one could measure the time it took until a user clicked a document, where the intuition again is that it is better if users spend less time to find what they were looking for. All these signals, and often many of them combined, are good indicators of performance of a search engine. Moreover, these interactions are the natural byproduct of users interacting with a search engine. When such interactions are used, explicit relevance judgments from trained assessors are no longer required. Without doing anything out of the ordinary, many search engines in fact already store all these interactions in their search logs. How to use these interactions for evaluation in a reliable and effective way is explored extensively in Part I of this thesis.
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Performance Evaluation of Selected Search Engines

Performance Evaluation of Selected Search Engines

A typical search engine is composed of three pipelined components (Arasu, Cho,Garcia- Molina, & Raghavan, 2001): a crawler, an indexer, and a query processor. The crawler component is responsible for locating, fetching, and storing the content residing within the Web. The downloaded content is concurrently parsed by an indexer and transformed into an inverted index (Tomasic, Garcia-Molina, & Shoens, 1994; Zobel, Moffat, & Sacks-Davis, 2002), which represents the downloaded collection in a compact and efficiently queryable form. The query processor is responsible for evaluating user queries and returning to the users the pages relevant to their query. The web crawling (downloading of web pages) is done by several distributed crawlers. There is an URL server that sends lists of URLs to be fetched to the crawlers. The web pages that are fetched are then sent to the store server. The store server then compresses and stores the web pages into a repository. Every web page has an associated ID number called a docID which is assigned whenever a new URL is parsed out of a web page. The indexing function is performed by the indexer and the sorter. The indexer performs a number of functions. It reads the repository, uncompresses the documents, and parses them. Each document is converted into a set of word occurrences called hits. The hits record the word, position in document, an approximation of font size, and capitalization. The indexer distributes these hits into a set of "barrels", creating a partially sorted forward index. The indexer performs another important function. It passes out all the links in every web page and stores important information about them in an anchors file. This file contains enough information to determine where each link points from and to, and the text of the link. The URLresolver reads the anchors file and converts relative URLs into absolute URLs and in turn into docIDs. It puts the anchor text into the forward index, associated with the docID that the anchor points to. It also generates a database of links which are pairs of docIDs. The links database is used to compute PageRanks for all the documents.
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A Survey on Web Search Engines

A Survey on Web Search Engines

The protocol was invented by a group governed by Mark P. McCahill at the University of Minnesota. It offers some features not natively supported by the Web and levy a much stronger hierarchy on facts stored on it. Its text menu interface is well-suited to computing environments that depend heavily on remote text-oriented computer terminals, which were still common at the time of its creation in 1991, and the ease of its protocol facilitated a wide variety of client implementations. More recent Gopher revisions and graphical users added support for multimedia. Gopher was preferred by many network administrators for using fewer network supplies than Web services.
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Learn to Personalized Image Search from the Photo Sharing Websites

Learn to Personalized Image Search from the Photo Sharing Websites

2) We introduce User-specific Topic Modeling to map the query relevance and user preference into the same user-specific topic space. For performance evaluation, two resources involved with users’ social activities are employed. Experiments on a large scale Flickr dataset demonstrate the effectiveness of the proposed method.

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Knowledge Engineering in Search Engines

Knowledge Engineering in Search Engines

Search engine tools help users to filter and extract the desired information. They can provide Web pages or documents that are relevant to user queries in a fraction of a second. Although the results have been filtered, some of them still do not match what users are looking for. The problem is that machines cannot understand the meaning implied in content. These keyword-based search engines return results that match keywords of user inputs instead of latent semantics. This causes poor efficiency in keyword searches.

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Improving Search Engines via Classification

Improving Search Engines via Classification

The ranking model in the previous section is unsupervised. Recently, learn- ing to rank [86] has becomes very topical as it can potentially deliver state of the art performance. Here we conduct the preliminary experiments in order to learn to rank classes of search results. The algorithms we make use of are RankNet [25] and RankingSVM [73]. We note that both algorithms use a pair- wise approach, i.e.,a pair of documents are represented as feature vectors and the output is the pairwise preference between each pair of documents. Such pair- wise approaches suffer from some limitations. i.e., the positional information is invisible to their loss functions, and they ignore the fact that some documents (or document pairs) are associated with the same query [83], compared to the listwise approaches, such as ListNet [28]. Moreover, given a small number of features, those machine learning algorithms may not outperform conventional ranking algorithms. Our target is to evaluate the classification-based search , but to be complete, we conduct the following experiment and present the result here. In future, we plan to carry out some research by making use of larger query log data, more features and advanced algorithms on this topic.
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Ranking Techniques in Search Engines

Ranking Techniques in Search Engines

Abstract - The World Wide Web consists billions of web pages and huge amount of information available within the web pages. To retrieve required information from World Wide Web, search engines perform number of tasks based on their respective architecture. When a user refers a query to the search engine, it generally returns a large number of pages in response to the user’s query. To support the ordering of search results according to their importance and relevance to user’s query, various ranking algorithms are applied on the search results. this paper gives detailed comparison and analysis of different ranking algorithms: first is the Text Based Ranking, second is PageRank( the Google’s algorithm) algorithm; and the last being the Users Rank algorithm.
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Securing Oracle Database from Search Engines Attack

Securing Oracle Database from Search Engines Attack

S earch engines become the dangerous that threads Various web applications over the internet. These applications may be vulnerable by SQL injection attack. SQL injection is a basic attack used either to gain unauthorized access to a database or to retrieve information directly from the database[1]. Interactive Web applications that employ database services accept user inputs and use them to form SQL statements at runtime. During an SQL injection attack, an attacker might provide malicious SQL query segments as user input which could result in a different database request. By using SQL injection attacks, an attacker could thus obtain and/or modify confidential/sensitive information [2]. Vulnerability in web applications allows malicious users to obtain unrestricted access to private and confidential information. SQL injection is ranked at the top in web application attack mechanisms used by hackers to steal data from organizations. Hackers can take advantages due to flawed design, improper coding practices, improper validations of user input, configuration errors, or other weaknesses in the infrastructure[3]. An attack may be possible due to poor design, configuration mistakes, or poor written code of the web application. A threat can be harmful for database, control of web application, and other components of web application, that are needed to be protected from all types of threat. All types of
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INTRODUCTION TO WEB SEARCH ENGINES

INTRODUCTION TO WEB SEARCH ENGINES

The first Web search engine was "Wandex", a now-defunct index collected by the World Wide Web Wanderer, a web crawler developed by Matthew Gray at MIT in 1993. Another very early search engine, Aliweb, also appeared in 1993, and still runs today. The first "full text" crawler-based search engine was WebCrawler, which came out in 1994. Unlike its predecessors, it let users search for any word in any web page, which became the standard for all major search engines since. It was also the first one to be widely known by the public. Also in 1994 Lycos (which started at Carnegie Mellon University) came out, and became a major commercial endeavor.
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Ranking Techniques in Search Engines

Ranking Techniques in Search Engines

• GIS Services for PDA Users: GIS real-time services are provided to PDA users by GIS applications running in the PDA, using the GIS dataset loaded into the PDA; and using road closure information and dispatch notifications received from CAD. PDA do not access the GIS data GIS server located at the Operation Room Instead, each PDA associated with the installation stores one or more installation specific GIS datasets, selected in accordance with the installation(s) where the unit responds. The installation- specific GIS dataset contains complete data for the installation GIS services for PDA user’s centre on the real-time map, which shows the following:
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Ranking Techniques in Search Engines

Ranking Techniques in Search Engines

First of all in introduction we will take a look about Information security management is the framework for ensuring the effectiveness of information security controls over information resources to ensure no repudiation, authenticity, confidentiality, integrity and availability of the information. Organizations need a systematic approach for information security management that addresses security consistently at every level. However, the security infrastructure of most organizations came about through necessity rather than planning, a reactive-based approach as opposed to a proactive approach[1]. Intrusion detection systems, firewalls, anti-virus software, virtual private networks, encryption and biometrics are security technologies in use today. Many devices and systems generate hundreds of events and report various problems or symptoms. Also, these devices may all come at different times and from different vendors, with different reporting and management capabilities and—perhaps worst of all—different update schedules. The security technologies are not integrated, and each technology provides the information in its own format and meaning. In addition, these systems across versions, product lines and vendors may provide little or no consistent characterization of events that represent the same symptom. Also, the systems are not efficient and scalable because they rely on human expertise to analyze periodically the data collected with all these systems. Network administrators regularly have to query different databases for new vulnerabilities and apply patches to their systems to avoid attacks. Quite often, different security staff is responsible and dedicated for the monitoring and analysis of data provided by a single system. Security staff does not periodically analyze the data and does not timely communicate analysis reports to other staff. The tools employed have very little impact on security prevention, because these systems lack the capability to generalize, learn and adapt in time.
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Exploring the Advances in Semantic Search Engines

Exploring the Advances in Semantic Search Engines

There is one common idea in the majority of approaches, that is, the machines must understand the meaning behind the Query and Data sources in order to return answers based on the meaning. Maybe this is the main requirement for a SSE. In- tuitively we can say that many SSE will be based on a similar core, including con- ceptual structures such as ontologies, and founded on main components to process queries in form of natural language. A direct consequence is the need to develop SSE allowing the users to play a part of the answers, before and after the query, that is, pre-query disambiguation, advertisement of ambiguity presence, and feed- back to improve futures answers.
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Ranking Techniques in Search Engines

Ranking Techniques in Search Engines

The retailers in India have to learn both the art and science of retailing by closely following how retailers in other parts of the world are organizing, managing, and coping up with new challenges in an ever-changing marketplace. Indian retailers must use innovative retail formats to enhance shopping experience, and try to understand the regional variations in consumer attitudes to retailing. Retail marketing efforts have to improve in the country advertising, promotions, and campaigns to attract customers; building loyalty by identifying regular shoppers and offering benefits to them; efficiently managing high-value customers; and monitoring customer needs constantly, are some of the aspects which Indian retailers need to focus upon on a more pro-active basis.
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Ranking Techniques in Search Engines

Ranking Techniques in Search Engines

A MANET is an autonomous group of mobile users that communicate over reasonably slow wireless links. The network topology may vary rapidly and unpredictably over time because the nodes are mobile. The network is decentralized where all network activity, including discovering the topology and delivering messages must be executed by the nodes themselves. Hence routing functionality will have to be incorporated into the mobile nodes. Mobile ad hoc network is a collection of independent mobile nodes that can communicate to each other via radio waves. The mobile nodes can directly communicate to those nodes that are in radio range of each other, whereas others nodes need the help of intermediate nodes to route their packets. These networks are fully distributed, and can work at any place without the aid of any infrastructure. This property makes these networks highly robust.
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Web Usage Mining in Search Engines

Web Usage Mining in Search Engines

There are few papers that deal with the use of query logs to improve search engines, because this infor- mation is usually not disclosed. The exceptions deal with strategies for caching the index and/or the answers [26, 16, 14] and query clustering using click-through data associated to queries (obtaining a bipartite graph) for ranking or related goals [10, 7, 24, 28, 27]. Other papers are focused in user behavior while searching, for example detecting the differences among new and expert users or correlating user clicks with Web struc- ture [11, 2, 15]. Recently, there has been some work on finding queries related to a website [9] and weight different words in the query to improve ranking [18].
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The Extraction of Social Networks from Web Using Search Engines

The Extraction of Social Networks from Web Using Search Engines

The automatic generation of social network through concepts extraction from web and also the development and growth of semantic web is the main incentive to create such networks in different domains. One of the main problems in this area is the access to a valid and perfect vocabulary collection for the production of such networks. In this research, an automatic method is proposed for the production of social network for research domain in computer sciences, using a sampling method of first pages, text processing algorithms, and also information retrieval techniques. This method is based on the structure of the social network under web and also based on data obtained from the results of the userssearch in their favorite fields such as articles, people, conferences, and books related to the subject of the research. One of the objectives of this research is to automatically provide a great collection of vocabulary and main concepts with a high and acceptable accuracy in order to facilitate and accelerate the production of network. For this purpose, relevant pages to the social network have been extracted, using a crawler.
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The Economics of Search Engines Search, Ad Auctions & Game Theory

The Economics of Search Engines Search, Ad Auctions & Game Theory

Advertisers have many opportunities to customize their keyword campaigns e.g. by limiting their campaign to a chosen area or region. Assuming that one is only interesting in potential customers located in Copenhagen one can target ads to the Copenhagen area. This means that ads will only be displayed if the users‟ IP address is from the Copenhagen area. This is an effective technique for advertisers to target a narrow segment of the market and limit the competition for certain keywords. Morkovich (2005) outlays endless opportunities in which one can customize search engine marketing campaigns. For example if one does not want to be associated with “expensive laptops” the queries containing the word “expensive” together with “laptops” can be sorted out. Also it might turn out that consumers mostly buy laptops during weekends. In this case advertisers can choose to only have their ads shown during weekends. This is beneficial if one has a limited budget and wants to have the most value for their money.
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Determining Bias to Search Engines from Robots.txt

Determining Bias to Search Engines from Robots.txt

More importantly, as websites may favor or disfavor cer- tain robots by assigning to them different access policies, this bias can lead to a “rich get richer” situation whereby some popular search engines are granted exclusive access to certain resources, which in turn could make them even more popular. Considering the fact that users often prefer a search engine with broad (if not exhaustive) information coverage, this “rich get richer” phenomenon may introduce a strong influence on users’ choice of search engines, which will eventually be reflected in the search engine market share. On the other hand, since it is often believed (although this is an exaggeration) that “what is not searchable does not exist,” this phenomenon may also introduce a biased view of the information on the Web.
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Document representation for efficient search engines

Document representation for efficient search engines

When a query whose results are cached is issued, the results are served from memory without any query processing. Where a query is not in the results cache, however, query processing must still take place. Hence memory management for the other data-structures involved in query processing is still an important problem. Repeated queries have high temporal locality, however query terms are commonly shared between queries giving repeated terms an even higher temporal locality. Analysing several large commercial search engine query logs, Xie and O’Hallaron [2002] and Baeza-Yates et al. [2008] independently show that queries submitted by a large number of users have a small lexicon. Our findings in Chapter 5 support this analysis, which show that repetition rate of query terms is significantly more than the repetition rate of whole queries, and that the number of unique terms is much smaller than the number of unique queries. Given the skew distribution of term occurrence in queries, it would seem that caching inverted lists should provide time savings.
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A Survey on Sponsored Search Advertising in Large Commercial Search Engines

A Survey on Sponsored Search Advertising in Large Commercial Search Engines

Bidding expressivity concerns how to best translate advertiser needs into an appropriate bidding language. For example, a wine producer in Sacramento may want to target its ads only to users located in the state of California. Commercial search engines allow the advertisers to fine-tune their ads by targeting 1) specific locations, 2) days of the week, 3) time of day, 4) demographic (gender and age) groups, and 5) languages. Obviously, a more expressive bidding language may be better tailored to the user needs, but comes at a high complexity cost of the auctions and the middleman software. Recently, more expressive bidding languages have been investigated. Even-Dar et al. introduce in [28] context-based auctions where advertisers can bid on keywords that satisfy specific contexts such as gender, income, likely task, etc. They further show that under certain conditions the overall social welfare increases when moving from standard to context- based mechanisms. Martin et al. [57] propose multi-feature auctions that enable advertisers to express bids on multiple features, namely, clicks, conversions, and slot positions. For instance, an advertiser may express that they only wish to be placed in prominent positions; or, they may prefer their ads to be placed near the top or bottom of the list, but not in the middle; or, they may value purchases (conversions) but have zero valuations for clicks alone. In the multi-feature model, the advertiser declares a bid table that summarizes their valuation over different combinations of the tree features; an efficient, scalable, and parallelizable infrastructure on the search engine’s side is responsible for ad ranking and pricing. To account for ad externalities, Ghosh et al.
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