Improve Search Engine Research Articles

Jail-Phish: An improved search engine based phishing detection system

Stealing of sensitive information (username, password, credit card information and social security number, etc.) using a fake webpage that imitates trusted website is termed as phishing. Recent techniques use search engine based approach to counter the phishing attacks as it achieves promising detection accuracy. But, the limitation of this approach is that it fails when phishing page is hosted on compromised server. Moreover, it also results in low true negative rate when newly registered or non-popular domains are encountered. Hence, in this paper, we propose an application named as Jail-Phish, which improves the accuracy of the search engine based techniques with an ability to detect the Phishing Sites Hosted on Compromised Servers (PSHCS) and also detection of newly registered legitimate sites. Jail-Phish compares the suspicious site and matched domain in the search results for calculating the similarity score between them. There exists some degree of similarity such as logos, favicons, images, scripts, styles, and anchorlinks within the pages of the same website whereas on the other side, the dissimilarity within the pages is very high in PSHCS. Hence, we use the similarity score between the suspicious site and matched domain as a parameter to detect the PSHCS. From the experimental results, it is observed that Jail-Phish achieved an accuracy of 98.61%, true positive rate of 97.77% and false positive rate less than 0.64%.

Coverage pattern based framework to improve search engine advertising

Sponsored search has emerged as one of the most dominant forms for advertising on the Web. In sponsored search, advertisers create ad campaigns and bid on the keywords of potential search queries related to a given product or service. It has been observed that search queries follow a long-tail distribution of a small yet fat head of frequent queries and a long and thin tail of infrequent queries. Normally, the advertisers tend to bid on frequent keywords related to search queries. As a result, the ad space of the tail portion of search queries is harder to exploit. In this paper, we have proposed an improved allocation approach to utilize the ad space of the tail keywords related to search queries based on the knowledge of coverage patterns extracted from the transactions formed from search query logs. The advertisers bid on potential concepts represented by coverage patterns which consist of a combination of head and tail keywords. By facilitating the advertisers to bid on the concepts, the proposed approach improves the ad space utilization of tail queries. Experiments on the real-world dataset of search query logs demonstrate that the proposed approach indeed improves the performance of search engine advertising by improving ad space utilization of tail queries.

Improving search engine optimization (SEO) by using hybrid modified MCDM models

Search engine optimization (SEO) has been considered one of the most important techniques in internet marketing. This study establishes a decision model of search engine ranking for administrators to improve the performances of websites that satisfy users’ needs. To probe into the interrelationship and influential weights among criteria of SEO and evaluate the gaps of performance to achieve the aspiration level in real world, this research utilizes hybrid modified multiple criteria decision-making models, including decision-making trial and evaluation laboratory (DEMATEL), DEMATEL-based analytic network process (called DANP), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The empirical findings discover that the criteria of SEO possessed a self-effect relationship based on DEMATEL technique. According to the influential network relation map (INRM), external website optimization is the top priority dimension that needs to be improved when implementing SEO. Among the six criteria for evaluation, meta tags is the most significant criterion influencing search engine ranking, followed by keywords and website design. The evaluation of search engine ranking reveals that the website with lowest gap would be the optimal example for administrators of websites to make high ranking website during the time that this study is executed.

User behavior analysis-based smart energy management for webpage ranking: Learning automata-based solution

Search engines are widely used for surfing the Internet. Different search engines vary with respect to their accuracy and time to fetch the information from the distributed/centralized database repository across the globe. However, it has been found in the literature that webpage ranking helps in saving the user's surfing time which in turn saves considerable energy consumption during computation and transmission across the network. Most of the earlier solutions reported in the literature uses the hyperlink structure of graph which consume a lot of energy during the computation. It may lead to the link leakage problem with the occurrence of spam pages more often. Nowadays, hyperlink structure alone is inadequate for predicting webpage importance keeping in view of the energy consumption of various smart devices. User browsing behavior depicts its real importance. It is essential to demote the spam pages to increase the search engine accuracy and speed. Hence, user behavior analysis along with demotion of spam pages can improve Search Engine Result Pages (SERP) which in turn saves the energy consumption. In the proposed approach, web page importance score is computed by analyzing user surfing behavior attributes, dwell time, and click count. After computing the webpage importance score, the ranks are revised by implementing it in Learning Automata (LA) environment. Learning automaton is the stochastic system which learns from the environment and responds either with a reward or a penalty. With every response from the environment, the probability of visiting the webpage is updated. Probability computation is done using Normal and Gamma distribution functions. In the proposal, we have considered only the dangling pages for experiments. Inactive webpages are punished and degraded from the system. We have validated proposed approach with Microsoft Learning to Rank dataset. It has been found in the experiments performed that 3403 dangling pages out of 12211 dangling pages have been degraded using the proposed scheme. The objective of the proposed scheme is achieved by saving web energy and computational cost. It takes 100 iterations to convergence which executed in 21.88ms. However, the user behavior analysis helped in improving PageRank score of the webpages.

Beyond the mandates: The far‐reaching benefits of multimedia accessibility

Key points Convergence of international standards has made requirements for accessible multimedia content clearer. Legal and commercial pressure for accessible content is mounting for higher education institutions (HEIs) and publishers as highlighted by recent high‐profile settlements in North America. Accessibility requirements for multimedia can be met by setting realistic, progressive goals and focusing resources on key content. Multimedia accessibility benefits range from wider audiences and increased user engagement to greater discoverability and improved search engine optimization. Vendors are ramping up their offerings for accessible production, and costs are going down.

A Novel Text Mining Method for User Context Extraction to Improve Search Engine Results Ranking

A Novel Text Mining Method for User Context Extraction to Improve Search Engine Results Ranking

Stronger Together: Embracing Google and Linked Data in Law Libraries

ABSTRACTThe MARC format ushered in the age of automation for libraries. However, the standard is dated. The Library of Congress began developing BIBFRAME, a linked data ontology, five years ago to supplant this aging standard. BIBFRAME isn't merely a replacement for MARC; it is the dawn of a new age of automation. Coupled with improved search engines and the open access movement, linked data can revolutionize the way all libraries do business. This article provides an overview of linked data technologies, surveys the early results of a linked data initiative, and argues for the adoption of this technology in law libraries.

A machine learning approach for result caching in web search engines

A commonly used technique for improving search engine performance is result caching. In result caching, precomputed results (e.g., URLs and snippets of best matching pages) of certain queries are stored in a fast-access storage. The future occurrences of a query whose results are already stored in the cache can be directly served by the result cache, eliminating the need to process the query using costly computing resources. Although other performance metrics are possible, the main performance metric for evaluating the success of a result cache is hit rate. In this work, we present a machine learning approach to improve the hit rate of a result cache by facilitating a large number of features extracted from search engine query logs. We then apply the proposed machine learning approach to static, dynamic, and static-dynamic caching. Compared to the previous methods in the literature, the proposed approach improves the hit rate of the result cache up to 0.66%, which corresponds to 9.60% of the potential room for improvement.

An ant-colony based approach for real-time implicit collaborative information seeking

We propose an approach based on Swarm Intelligence — more specifically on Ant Colony Optimization (ACO) — to improve search engines’ performance and reduce information overload by exploiting collective users’ behavior. We designed and developed three different algorithms that employ an ACO-inspired strategy to provide implicit collaborative-seeking features in real time to search engines. The three different algorithms — NaïveRank, RandomRank, and SessionRank — leverage on different principles of ACO in order to exploit users’ interactions and provide them with more relevant results. We designed an evaluation experiment employing two widely used standard datasets of query-click logs issued to two major Web search engines. The results demonstrated how each algorithm is suitable to be employed in ranking results of different types of queries depending on users’ intent.

Measuring and Predicting Search Engine Users’ Satisfaction

Search satisfaction is defined as the fulfillment of a user’s information need. Characterizing and predicting the satisfaction of search engine users is vital for improving ranking models, increasing user retention rates, and growing market share. This article provides an overview of the research areas related to user satisfaction. First, we show that whenever users choose to defect from one search engine to another they do so mostly due to dissatisfaction with the search results. We also describe several search engine switching prediction methods, which could help search engines retain more users. Second, we discuss research on the difference between good and bad abandonment, which shows that in approximately 30% of all abandoned searches the users are in fact satisfied with the results. Third, we catalog techniques to determine queries and groups of queries that are underperforming in terms of user satisfaction. This can help improve search engines by developing specialized rankers for these query patterns. Fourth, we detail how task difficulty affects user behavior and how task difficulty can be predicted. Fifth, we characterize satisfaction and we compare major satisfaction prediction algorithms.

PRS Global Open's Evolution: Logo, Growth, and Mission.

PRS Global Open's Evolution: Logo, Growth, and Mission.

Search engine optimization

Purpose – The purpose of this paper is to examine the effect of, and identify core techniques of, search engine optimization (SEO) techniques applied to the web (http://lg-sl.net) and mobile (http//m.lg-sl.net) Science Land content and services at LG Sangnam Library in Korea. Design/methodology/approach – In accordance with three major SEO guidelines, ten SEO techniques were identified and applied, and their implications were extracted on three areas: improved search engine accessibility, increased relevance between site content and search engine keywords, and improved site credibility. The effects were quantitatively analyzed in terms of registered search engine keywords and influx of visits via search engines. Findings – This study shows that SEO techniques help increase the exposure of the library services and the number of visitors through search engines. Practical implications – SEO techniques have been applied to a few non-Korean information service organizations, but it is not a well-accepted practice in Korean libraries. And the dominant search engines in Korea have published their own SEO guidelines. Prior to this study, no significant endeavors have been undertaken in the context of Korean library services that have adopted SEO techniques to boost exposure of library services and increase user traffics. Originality/value – This is the first published study that has applied optimized SEO techniques to Korean web and mobile library services, in order to demonstrate the usefulness of the techniques for maximized exposure of library content.

Inferring User Search Goals with feedback Sessions using Fuzzy K-Means Algorithm

This document shows the concept for a broad topic and ambiguous query, different types of users may have different search goals when they submit the query to the search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance information and user experience. In this paper, we propose a novel approach to infer user search goals by analyzing search engine query logs. First, we propose a framework to search different user search goals for a query by making cluster to the proposed feedback sessions. Feedback sessions are constructed from user click-through logs i.e. user response and can efficiently reflect the information needs to users. Second, we propose a novel approach to create pseudo-documents to better represent the feedback sessions for clustering. Finally, we propose a new criterion Classified Average Precision (CAP) to calculate the performance of inferring user search goals. Experimental results are presented using user click-through logs from a commercial search engine to check the effectiveness of our proposed methods

Analysis of chinese‐english mixed language query reformulation strategies and patterns during web searching

ABSTRACTWith the increasing number of multilingual webpages on the Internet, cross‐language information retrieval has become an important research issue. In cultures where people speak both Chinese and English, using mixed language in oral speaking and web searching is a common phenomenon. This study employs semi‐structured interviews and query log analysis to examine the query reformulation strategies and patterns regarding Chinese‐English mixed language queries, and to determine why search engine users utilize certain reformulation strategies. The results can inform information scientists and IR system designers to enhance cross‐language controlled vocabularies and develop discovery platforms for multilingual content, and improve search engines to provide users with more relevant results. The findings could also be expanded to other language combinations that would improve search engine designs.

Passage-Based Bibliographic Coupling: An Inter-Article Similarity Measure for Biomedical Articles.

Biomedical literature is an essential source of biomedical evidence. To translate the evidence for biomedicine study, researchers often need to carefully read multiple articles about specific biomedical issues. These articles thus need to be highly related to each other. They should share similar core contents, including research goals, methods, and findings. However, given an article r, it is challenging for search engines to retrieve highly related articles for r. In this paper, we present a technique PBC (Passage-based Bibliographic Coupling) that estimates inter-article similarity by seamlessly integrating bibliographic coupling with the information collected from context passages around important out-link citations (references) in each article. Empirical evaluation shows that PBC can significantly improve the retrieval of those articles that biomedical experts believe to be highly related to specific articles about gene-disease associations. PBC can thus be used to improve search engines in retrieving the highly related articles for any given article r, even when r is cited by very few (or even no) articles. The contribution is essential for those researchers and text mining systems that aim at cross-validating the evidence about specific gene-disease associations.

Marketing Finding Aids on Social Media: What Worked and What Didn't Work

Abstract Sam Houston State University's Special Collections (SHSU) needed a way to expose finding aids to more users. Using social media to promote online awareness, while simultaneously improving search engine result rankings for the finding aids, seemed like a potential solution to this problem. With this goal in mind, SHSU researchers selected ten social media sites to test the assumption that posting information about finding aids to social media would be an effective marketing strategy. Following three months of posting information about finding aids while tracking user traffic to finding aids from social media sites, the research findings indicate that a combination of certain social media sites, in this case WordPress, Facebook, and Twitter, provides a better marketing strategy for getting the word out about archival collections. Additionally, the researchers confirm that posting to social media improves search engine result rankings for the finding aids. Overall, the SHSU researchers' results conc...

A New Algorithm for Inferring User Search Goals with Feedback Sessions

For a broad-topic and ambiguous query, different users may have different search goals when they submit it to a search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance and user experience. In this paper, we propose a novel approach to infer user search goals by analyzing search engine query logs. First, we propose a framework to discover different user search goals for a query by clustering the proposed feedback sessions. Feedback sessions are constructed from user clickthrough logs and can efficiently reflect the information needs of users. Second, we propose a novel approach to generate pseudo-documents to better represent the feedback sessions for clustering. Finally, we propose a new criterion “Classified Average Precision (CAP)” to evaluate the performance of inferring user search goals. Experimental results are presented using user click-through logs from a commercial search engine to validate the effectiveness of our proposed methods.

Sources of traffic and visitors' preferences regarding online public reports of quality: web analytics and online survey results.

BackgroundIn the context of the Affordable Care Act, there is extensive emphasis on making provider quality transparent and publicly available. Online public reports of quality exist, but little is known about how visitors find reports or about their purpose in visiting.ObjectiveTo address this gap, we gathered website analytics data from a national group of online public reports of hospital or physician quality and surveyed real-time visitors to those websites.MethodsWebsites were recruited from a national group of online public reports of hospital or physician quality. Analytics data were gathered from each website: number of unique visitors, method of arrival for each unique visitor, and search terms resulting in visits. Depending on the website, a survey invitation was launched for unique visitors on landing pages or on pages with quality information. Survey topics included type of respondent (eg, consumer, health care professional), purpose of visit, areas of interest, website experience, and demographics.ResultsThere were 116,657 unique visitors to the 18 participating websites (1440 unique visitors/month per website), with most unique visitors arriving through search (63.95%, 74,606/116,657). Websites with a higher percent of traffic from search engines garnered more unique visitors (P=.001). The most common search terms were for individual hospitals (23.25%, 27,122/74,606) and website names (19.43%, 22,672/74,606); medical condition terms were uncommon (0.81%, 605/74,606). Survey view rate was 42.48% (49,560/116,657 invited) resulting in 1755 respondents (participation rate=3.6%). There were substantial proportions of consumer (48.43%, 850/1755) and health care professional respondents (31.39%, 551/1755). Across websites, proportions of consumer (21%-71%) and health care professional respondents (16%-48%) varied. Consumers were frequently interested in using the information to choose providers or assess the quality of their provider (52.7%, 225/427); the majority of those choosing a provider reported that they had used the information to do so (78%, 40/51). Health care professional (26.6%, 115/443) and consumer (20.8%, 92/442) respondents wanted cost information and consumers wanted patient narrative comments (31.5%, 139/442) on the public reports. Health care professional respondents rated the experience on the reports higher than consumers did (mean 7.2, SD 2.2 vs mean 6.2, SD 2.7; scale 0-10; P<.001).ConclusionsReport sponsors interested in increasing the influence of their reports could consider using techniques to improve search engine traffic, providing cost information and patient comments, and improving the website experience for both consumers and health care professionals.

IJARCCE - Computer and Communication Engineering

2 Abstract: For a broad-topic and ambiguous query, different users may have different search goals when they submit it to a search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance and user experience. The project proposes a novel approach to infer user search goals by analyzing search engine query logs. First, it proposes a framework to discover different user search goals for a query by clustering the proposed feedback sessions. The feedback session is defined as the series of both clicked and unclicked URLs and ends with the last URL that was clicked in a session from user click-through logs. Second, the pseudo-documents are produced to better represent the feedback sessions for clustering. The pseudo-documents are clustered using Fuzzy C Means, the fuzzy similarity based self- constructing algorithm. A novel optimization method is used to map feedback sessions to pseudo-documents which can efficiently reflect user information needs and finally, a new criterion Classified Average Precision (CAP) is used to evaluate the performance of inferring user search goals. Experimental results are presented using user click-through logs from a commercial search engine to validate the effectiveness.

English

In real scenario when users submit a search query to a search engine, each user may have different search goals. We can improve search engine relevance by analyzing user search goals. We present an approach that infers user search goals by analyzing search engine query logs. Our approach discovers different user search goals for a query by clustering the proposed feedback sessions. Users information needs can be captured with the help of feedback sessions. Feedback sessions are constructed from user click-through logs and can efficiently reflect the information needs of users on results. Secondly, we also generate pseudo-documents for better representation of the feedback sessions for clustering. Finally, we present Classified Average Precision (CAP) to evaluate the performance of inferring user search goals. For an ambiguous query, different users may have different search goals when they submit it to a search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance and user experience. In this paper, we present a novel approach to Enhance Search Result For User Query Using Iterative User Feedback.