Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Search engines have become one of the primary tools for travel planning and, as such, have become an important element in the marketing efforts of destination marketing organizations. Recently, it has been demonstrated that because of the dynamic relationships among search engine providers, the tourism industry, and travelers, it is essential that destination marketing organizations understand how search engines are used by travelers within the travel planning process. This study proposed a three-stage model that provides a framework for examining how online travelers use search engines and how different aspects of the travel planning process shape this use. The model was evaluated based on a national survey of American online travel planners, and the findings provide significant insight into the role of search engines for travel planning. The implications of the study for search engine marketing are discussed as well as several challenges for future research.

A Framework of Search Engine Use for Travel Planning

In this paper, we propose a system for helping users to judge the credibility of Web search results and to search for credible Web pages. Conventional Web search engines present only titles, snippets, and URLs for users, which give few clues to judge the credibility of Web search results. Moreover, ranking algorithms of the conventional Web search engines are often based on relevance and popularity of Web pages. Towards credibility-oriented Web search, our proposed system provides users with the following three functions: (1) calculation and visualization of several scores of Web search results on the main credibility aspects, (2) prediction of user's credibility judgment model through user's credibility feedback for Web search results, and (3) re-ranking of Web search results based on user's predicted credibility model. Experimental results suggest that our system enables users - in particular, users with knowledge about search topics - to find credible Web pages from a list of Web search results more efficiently than conventional Web search interfaces.

Enhancing credibility judgment of web search results

One of the main tasks when creating and maintaining knowledge bases is to validate facts and provide sources for them in order to ensure correctness and traceability of the provided knowledge. So far, this task is often addressed by human curators in a three-step process: issuing appropriate keyword queries for the statement to check using standard search engines, retrieving potentially relevant documents and screening those documents for relevant content. The drawbacks of this process are manifold. Most importantly, it is very time-consuming as the experts have to carry out several search processes and must often read several documents. In this article, we present DeFacto (Deep Fact Validation) --- an algorithm for validating facts by finding trustworthy sources for it on the Web. DeFacto aims to provide an effective way of validating facts by supplying the user with relevant excerpts of webpages as well as useful additional information including a score for the confidence DeFacto has in the correctness of the input fact.

/pdf/defacto-deep-fact-validation-5emaww40o7.pdf

DeFacto - deep fact validation

Previous research indicates that web users rely to a great extent on the ranking provided by the search engine and predominantly access the first few web pages presented. In case that the information sources presented in the top of the search engine results page (SERP) are of rather low trustworthiness, this might lead to a biased or incomplete view of the topic—especially when dealing with controversial issues. Study 1, thus, systematically investigated whether participants who were asked to search for an unfamiliar and controversial medical issue accessed fewer trustworthy information sources and consequently included less information from trustworthy pages in their argumentation when the search results were ranked from least to most trustworthy on a Google-like SERP than when they were ranked from most to least trustworthy. Results from Study 1 confirmed these assumptions. Furthermore, Study 2 showed that when the same materials were presented in a grid interface, the impact of the position of the sear...

The Role of Search Result Position and Source Trustworthiness in the Selection of Web Search Results When Using a List or a Grid Interface

Increased usage of Web search engines in our daily lives means that the trustworthiness of searched results has become crucial User studies on the usage of search engines and analysis of the factors used to determine trust that users have in search results are described in this paper Based on the analysis, we developed a system to help users determine the trustworthiness of Web search results by computing and showing each returned page's topic majority, topic coverage, locality of supporting pages (ie, pages linked to each search result) and other information The measures proposed in the paper can be applied to the search of Web-based libraries or can be useful in the usage of digital library search systems

/pdf/trustworthiness-analysis-of-web-search-results-50kclsqqmp.pdf

Trustworthiness analysis of web search results

The rapidly increasing use of geographically restrictive web search has made determination of the geographic relevance of web content an important task. We have developed a method that uses Gaussian mixture models to estimate the geographic relevance of web pages and arbitrary topics and have implemented a visualization interface that maps pages and topics to geographic space. The system enables the user to retrieve web pages and topics expressed on the Web that are relevant to an arbitrary geographic area.

Estimation of Geographic Relevance for Web Objects Using Probabilistic Models

Since our daily lives strongly depend on information obtained by Web search, the credibility of Web search results has become crucial. An important aspect of the credibility of search results is regionality of Web pages. In this paper, we propose a system for helping users assess the credibility of search results by measuring and presenting the regionality of support to Web pages. We conceive two different types of measures for evaluating "geographical social support": the uniformity of support and the proximity of support. The uniformity of geographical support (US) indicates uniformity of geographic distribution of Web pages linking to a Web page. It is calculated by using the Kullback-Leibler (KL) divergence. The proximity of geographical support (PS) express how a page is supported by pages geographically located close to the page. We describe our implemented prototype system that shows the two measures for Web search results.

Web information credibility analysis by geographical social support

As an index of reliability evaluation of web pages, we introduce "regional supportness" which indicates how regional area a web page and web object is supported from. Web object is , for examle, local features in web page. Supportness to pages approximates hyperlink, and we utilize its spreading degree for support of judgement of reliability. Moreover, we describe a method of object-level search based on regional supportness and its evaluation.

/pdf/reliability-evaluation-of-web-pages-and-object-level-search-2yhaeltkrf.pdf

Hiroyuki Kondo

Papers

Trustworthiness analysis of web search results

Estimation of Geographic Relevance for Web Objects Using Probabilistic Models

Web information credibility analysis by geographical social support

Reliability Evaluation of Web Pages and Object-level Search Based on Regional Supportness