Query Expansion Based on a Personalized Web Search Model

Various search services quality on the Internet can be improved by personalized web search. Users face sort of dissatisfaction when the results fetched by search engines are not related to the query they have asked for. This irrelevance result is retrieved huge based on the enormous variety of consumers’ perspective and backgrounds, as well as the ambiguity of the contents. However, evidences show that the user’s private information which they search has become public due to the proliferation of Personalized Web Search. The proposed framework RPS implement re-ranking technique, which adaptively make simpler user profiles by queries while respecting the consumer particular constraints of privacy. The great challenge in personalized web search is Privacy protection. To increase the efficiency and accuracy of web search privacy we use Greedy IL algorithm, i.e. GreedyDP and GreedyIL, for runtime generalization. Experiment assessment results show that the privacy-preserving personalized framework and re-ranking approach is highly effective and accurate enough for user profiling privacy personalization on the web search.

Variability of patient spine education by Internet search engine

Purpose — The purpose of this chapter is to give an overview of the context of Web search and search engine related research, as well as to introduce the reader to the sections and chapters of the book. Methodology/approach — We review literature dealing with various aspects of search engines, with special emphasis on emerging areas of Web searching, search engine evaluation going beyond traditional methods and new perspectives on Web searching. Findings — The approaches to studying Web search engines are manifold. Given the importance of Web search engines for knowledge acquisition, research from different perspectives needs to be integrated into a more cohesive perspective. Research limitations/implications — The chapter suggests a basis for research in the field and also introduces further research directions. Originality/value of paper — The chapter gives a concise overview of the topics dealt within the book and also shows directions for researchers interested in Web search engines.

Personalization promises to improve the accuracy of Web search and has been drawing much research attention recently. Some evidence indicates that for educational purposes, the disadvantages of personalized search are not justified by its benefits. The potential issues with search personalization, especially in an educational context, include loss of serendipity and capability, commercialization of education and the Filter Bubble effect where users are denied information if search engine algorithms decide it is irrelevant to them. The majority of students in higher education make use of general-purpose search engines to find academic information, however we have little knowledge about the effects of personalization on learners' experience and achievements. This observation motivates the research in this paper. First, we surveyed 120 university students to investigate which research sources, including search engines they predominately use and how much they depend on each for educational purposes. We learned that the majority of students prefer Google to other search engines; indeed sometimes it is their primary or only information-seeking tool. Additionally, about 80% of them use search engines for educational purposes on daily basis. Second, we measured the difference between personalized and non-personalized search results for 120 academic search queries divided equally into four categories: Education, IT, Health sciences and Business. Our results showed that on average only 53% of links appear, not necessarily in the same order, in both personalized and non-personalized search results. Interestingly, we observed only slight differences in the extent of personalization based on academic topics.

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In most major search engines, the interface for image search is the same as traditional Web search: a keyword query followed by a paginated, ranked list of results. Although many image search innovations have appeared in both the literature and on the Web, few have seen widespread use in practice. In this work, we explore the differences between image and general Web search to better support users’ needs. First, we describe some unique characteristics of image search derived through informal interviews with researchers, designers, and managers responsible for building and deploying a major Web search engine. Then, we present results from a large scale analysis of image and Web search logs showing the differences in user behaviour. Grounded in these observations, we present design recommendations for an image search engine supportive of the unique experience of image search. We iterate on a number of designs, and describe a functional prototype that we built.

Internet has become an instant source of informatio n for almost anyone. By viewing the results of sear ch terms displayed by an Internet Search Engine (ISE), a person may decide his next course of action whether to continue using the Internet, abort or to combine it with other sources of information. The results produced by an ISE can be regarded as an in dex of relative availability of references. Given a list of suitable search terms, a user will firstly have to decide on the ISE to be used. Due to huge potent ial references available for given search terms the use r has to create heuristics to choose the entries sh own on the computer screen. As there are varying breadths and depths of information revealed by various Inter net Search Engines (ISE’s), this paper will not attempt to make a comparison among ISE’s, rather will focu s only on a particular search engine and ascertain th e results it produces given a list of search terms. Google is chosen as a proxy, being one of the most popular ISE’s. By confining to only a search engine, the s tudy affords to control variability among ISE’s should m ultiple ISE’s be used. With the search engine place d under control, it is easy to achieve the primary ob jective of the study, i.e., to ascertain availabili ty of relative breadth of sub-themes of Analytic Hierarch y Process (AHP) in Google. It is natural for user, especially researcher to be concerned with number, quantity. If there is seemingly abundant literature , one would be motivated to pursue research along the the me or sub-theme. In this study, the strength of presence of a sub-theme is measured by using two measures: (i) result of a sub-theme of AHP over the sub-theme itself, and (ii) result of the sub-theme over the total results generated for all of the thi rty six sub-themes used in the search. This study controlle d biasness in specifying the sub-themes of AHP by adopting the sub-themes or search terms specified b y the 2013 AHP conference organizers. This decision helps make the study efficient without with it has to distill the sub-themes by surveying the AHP literature. The data for analysis was gathered by s urfing Google on 26 Feb 2013 8.55 p.m. - 9.26 p.m. Peninsular Malaysian time. The search results were computed to generate two types of ratios specified earlier. A composite index was created using the re sulting two types of ratios which are used to class ify the efficiency, hence dominance of the original res ults (hits). Kendall’s correlation produced statist ically significant correlations between the composite inde x and Rank of AHP specific and area results. Using indices greater than 1.000 as the base, 6 AHP specific areas occupy the top positions with ratios ranging from 19.341 to 61.574; 15 AHP specific areas occupy the second top positions with ratios ranging from 1.119 to 9.602, and 14 AHP specific areas occupy the third and last position with indi ces below 1.000 ranging from 0.050 to 0.894. The paper includes dis cussion, implications, limitations, conclusions and suggestions for further research.

Nowadays, as the usage of Internet has incredibly increased, web search engines become the common approach to find and retrieve needed information. Hence, evaluating search engine quality is a hot topic which attracts many researches' attention. In this paper, we propose a framework named Similarity based Automatic Web Search Engine Evaluation, SAWSEE, to evaluate web search engines. SAWSEE measures the information retrieval effectiveness of web search engines by comparing and voting their returned results, particularly using nDCG metric to rank search engines. SAWSEE compares the search engines' results based on their similarity which is calculated in two consecutive levels, the web page address level and the main content of web page level. To find the similarity of the main content of two search engines' results, SAWSEE utilizes Winnowing algorithm, a well-known and widely-used plagiarism detection method. We compared our method with the results acquired from human assessors' evaluations. The promising comparison shows that SAWSEE provides rankings that are consistent with the rankings resulted from human assessors' evaluations. Hence, the proposed method can be applied in real world environments for evaluation of web search engines.

We address the problem of irrelevant results for short queries on Web search engines using latent semantic indexing in the WordSpace model and query expansion. First, we predict the potential concept topics, which are the domains for the search terms. Next, we expand the search terms in each of the predicted domains in parallel. We then submit separate queries, specialized for each domain, to a general-purpose search engine. The user is presented with categorized search results under the predicted domains. We prepared a categorized text collection (corpus) using Web directory listing to build word association models. We compare the results obtained using this corpus with those using Reuters corpus. User evaluations indicate that our approach helps the users avoid having to examine irrelevant Web search results, especially with short queries

A fundamental problem with current Web search technology is that in the absence of any additional information, the same query provided by two different searchers will produce the same set of search results, even if the information needs of the searchers are different. Web search personalization has been proposed as a solution to this problem, whereby the interests and preferences of individual users are modelled and used to affect the outcomes of their subsequent searches. A common approach is to generate vector-based models of searchers' interests, and re-rank the search results based on the similarity of the documents to these models. In this paper, a novel approach is proposed to automatically identify and re-weight significant dimensions in vector-based models in order to improve the personalized order of Web search results. This approach is inspired by Luhn's model of term importance, which is rooted in Zipf's Laws. Evaluations with a set of ambiguous queries illustrate the effectiveness of this approach.

 Abstract—The web search engines are used to extract query specific information from this massive pool of WWW. A large number of different search engines are available to the user to satisfy their needs. Every search engine uses its own specific algorithm to rank the list of web pages returned by the search engine for the users query, so that the most relevant page appears first in the list. Users think which search engine should be selected for searching corresponding to any query topic for efficient search. For decision making on the basis of search result users want to know, whether they are significantly different or not. In this regard, a study of three popular different search engines (i.e. Google, Yahoo and Altavista) on twenty-four different query topics in terms of the quality of most relevant web page is done. The three parameters of the quality of the most relevant web page taken into consideration are: 'in depth coverage', 'ideation and clarity', and 'number of links of related articles to query topic'. For each query topic, the first web page returned by these three search engines were evaluated and graded by the experts of that specific area. Based on these grades, a table is maintained showing the relative performance of selected three search engines in terms of three parameters of quality of first returned web page and thus comparing the ranking algorithms used by them. We analyzed the result using analysis of variance and F test. By this analysis we found grade for first return page result corresponding to each and every topic through all these three search engines (Google, Yahoo and Altavista) does not vary significantly. Thus they are equivalent. So users can treat these three poular search engines as equivalent search engine for the first return page result on the basis of these three parameters.

Query expansion technology is usually used to make abundant query to search information. Traditional query expansion based on WordNet, user search log or user feedback couldn’t get the user’s real intent, sometimes the latter could expand overly. In this paper, we propose a novel query expansion method based both on the user’s long-term relation and short-term relation in XML social book search. We first propose recommendation degree of a tagged word based on a user’s long-term social relation and short-term relation respectively, and the combined recommendation degree of a tagged word, then based on these definitions, we propose a new query expansion algorithm. The experimental results on real-world data show that our method has greatly increased the retrieval precise and users’ satisfaction . Keywordsquery expansion; long-term social relation; short-term social relation; book search; recommendation degree

The purpose of this study is to provide results from experiments designed to investigate the cross‐validation of an artificial neural network application to automatically identify topic changes in Web search engine user sessions by using data logs of different Web search engines for training and testing the neural network. Sample data logs from the FAST and Excite search engines are used in this study. The results of the study show that identification of topic shifts and continuations on a particular Web search engine user session can be achieved with neural networks that are trained on a different Web search engine data log. Although FAST and Excite search engine users differ with respect to some user characteristics (e.g., number of queries per session, number of topics per session), the results of this study demonstrate that both search engine users display similar characteristics as they shift from one topic to another during a single search session. The key finding of this study is that a neural network that is trained on a selected data log could be universal; that is, it can be applicable on all Web search engine transaction logs regardless of the source of the training data log.

BackgroundAdolescents and young adults are disproportionately affected by HIV, suggesting that HIV prevention methods such as pre-exposure prophylaxis (PrEP) should focus on this group as a priority. As digital natives, youth likely turn to internet resources regarding health topics they may not feel comfortable discussing with their medical providers. To optimize informed decision-making by adolescents and young adults most impacted by HIV, the information from internet searches should be educational, accurate, and readable.ObjectiveThe aims of this study were to compare the accuracy of web-based PrEP information found using web search engines and virtual assistants, and to assess the readability of the resulting information.MethodsAdolescent HIV prevention clinical experts developed a list of 23 prevention-related questions that were posed to search engines (Ask.com, Bing, Google, and Yahoo) and virtual assistants (Amazon Alexa, Microsoft Cortana, Google Assistant, and Apple Siri). The first three results from search engines and virtual assistant web references, as well as virtual assistant verbal responses, were recorded and coded using a six-tier scale to assess the quality of information produced. The results were also entered in a web-based tool determining readability using the Flesch-Kincaid Grade Level scale.ResultsGoogle web search engine and Google Assistant more frequently produced PrEP information of higher quality than the other search engines and virtual assistants with scores ranging from 3.4 to 3.7 and 2.8 to 3.3, respectively. Additionally, the resulting information generally was presented in language at a seventh and 10th grade reading level according to the Flesch-Kincaid Grade Level scale.ConclusionsAdolescents and young adults are large consumers of technology and may experience discomfort discussing their sexual health with providers. It is important that efforts are made to ensure the information they receive about HIV prevention methods, and PrEP in particular, is comprehensive, comprehensible, and widely available.

SummaryDue to the exponential growth in the size of available data, people often prefer direct answers in many cases, instead of a set of ranked documents while searching using a search engine. Furthermore, people often do not provide enough information regarding their search. Query expansion would help in such cases so that queries would be best understood by search engines. Although the queries are expanded, many a times, they end up with wrong results due to the entity mentions in the questions. Hence, we need to disambiguate named entities properly so as to choose the right mention. In this paper, a method is proposed for named entity disambiguation in order to help in query expansion. An adapted Lesk similarity measure is used to find the similarity between questions and the target Wikipedia disambiguation pages. The proposed approach has shown very good results in disambiguating organization‐ and miscellaneous‐type entity mentions.