Navigating the Web: A Qualitative Eye Tracking-based Study of Translators’ Web Search Behaviour

This Element reports an investigation of translators’ use of web-based resources and search engines. The study adopted a qualitative eye tracking-based methodology utilising a combination of gaze replay and retrospective think aloud (RTA) to elicit data. The main contribution of this Element lies in presenting not only an alternative eye tracking methodology for investigating translators’ web search behaviour but also a systematic approach to gauging the reasoning behind translators’ highly complex and context-dependent interaction with search engines and the Web

Digital libraries for creative communities

Digital library technologies have a great deal to offer to creative, design communities. They can enable large collections of text, images, music, video and other information objects to be organised and accessed in interesting and diverse ways. Ordinary people—people not traditionally viewed as 'creators' or 'designers'—can now conceive, assemble, build, and disseminate new information collections. This paper explores the development rationale behind the Greenstone digital library technology. We also examine three examples of creative new techniques for accessing and presenting information in digital libraries and stress the importance of tailoring information access to support the requirements of the users and application area

Navigating the Web. A Qualitative Eye Tracking–Based Study of Translators' Web Search Behaviour

This Element reports an investigation of translators' use of web-based resources and search engines. The study adopted a qualitative eye tracking-based methodology utilising a combination of gaze replay and retrospective think aloud (RTA) to elicit data. The main contribution of this Element lies in presenting not only an alternative eye tracking methodology for investigating translators' web search behaviour but also a systematic approach to gauging the reasoning behind translators' highly complex and context-dependent interaction with search engines and the Web

Provision of Relevant Results on web search Based on Browsing History

Different users submit a query to a web search engine with different needs. The general type of search engines follows the "one size fits all" model which is not flexible to individual users resulting in too many answers for the query.  In order to overcome this drawback, in this paper, we propose a framework for personalized web search which considers individual's interest introducing intelligence into the traditional web search and producing only relevant pages of user interest. This proposed method is simple and efficient which ensures quality suggestions as well as promises for effective and relevant information retrieval. The framework for personalized web search engine is based on user past browsing history. This context is then used to make the web search more personalized. The results are encouraging

Inferring Networks of Substitutable and Complementary Products

In a modern recommender system, it is important to understand how products relate to each other. For example, while a user is looking for mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. These two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. Here we develop a method to infer networks of substitutable and complementary products. We formulate this as a supervised link prediction task, where we learn the semantics of substitutes and complements from data associated with products. The primary source of data we use is the text of product reviews, though our method also makes use of features such as ratings, specifications, prices, and brands. Methodologically, we build topic models that are trained to automatically discover topics from text that are successful at predicting and explaining such relationships. Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews.Comment: 12 pages, 6 figure

Personalised Web Search using Browsing History and Domain Knowledge

Different users have different needs when they submit a query to a web search engine. Personalized web search is able to satisfy individual’s information needs by modeling long-term and short-term user interests based on user past queries, actions and incorporate these in the search process. A Personalized Web Search has various levels of effectiveness for different contexts, queries, users etc. Personalized search has been a most important research area and many techniques have been developed and tested, still many challenges and issues are yet to be explored. This paper proposes a framework for building an Enhanced User Profile by using user's browsing history and improving it using domain knowledge. Enhanced User Profile is used for suggesting relevant web pages to the user. The results of experiments show that the suggestions provided to the user using Enhanced User Profile are better than those obtained by using a User Profile. DOI: 10.17762/ijritcc2321-8169.150315

Casual Information Visualization on Exploring Spatiotemporal Data

The goal of this thesis is to study how the diverse data on the Web which are familiar to everyone can be visualized, and with a special consideration on their spatial and temporal information. We introduce novel approaches and visualization techniques dealing with different types of data contents: interactively browsing large amount of tags linking with geospace and time, navigating and locating spatiotemporal photos or videos in collections, and especially, providing visual supports for the exploration of diverse Web contents on arbitrary webpages in terms of augmented Web browsing

Teaching Information Fluency: How to Teach Students to be Efficient, Ethical, and Critical Information Consumers

Searching is becoming easier than thinking. Enter a query in a search engine, and the searcher is instantly flooded with results. Information has never been easier to retrieve and consume. At the same time, determining the quality of the results remains a daunting task. Despite the attempts to make search tools brain dead easy 1 to use, searching that reduces the need to think invites problems. Machines cannot reliably predict what each individual is hunting for, machines cannot determine what is credible, yet that is the direction search engine development is headed

Understanding Children’s Help-Seeking Behaviors: Effects of Domain Knowledge

This dissertation explores children’s help-seeking behaviors and use of help features when they formulate search queries and evaluate search results in IR systems. This study was conducted with 30 children who were 8 to 10 years old. The study was designed to answer three research questions with two parts in each: 1(a) What are the types of help-seeking situations experienced by children (8-10 years old) when they formulate search queries in a search engine and a kid-friendly web portal?, 1(b) What are the types of help-seeking situations experienced by children (8-10 years old) when they evaluate search results in a search engine and a kid-friendly web portal?, 2(a) What types of help features do children (8-10 years old) use and desire when they formulate search queries in a search engine and a kid-friendly web portal?, 2(b) What types of help features do children (8-10 years old) use and desire when they evaluate search results in a search engine and a kid-friendly web portal?, 3(a) How does children’s (8-10 years old) domain knowledge affect their help seeking and use of help features when they formulate search queries in a search engine and a kid-friendly web portal?, 3(b) How does children’s (8-10 years old) domain knowledge affect their help seeking and use of help features when they evaluate search results in a search engine and a kid-friendly web portal? This study used multiple data collection methods including performance-based domain knowledge quizzes as direct measurement, domain knowledge self-assessments as indirect measurement, pre-questionnaires, transaction logs, think-aloud protocols, observations, and post-interviews. Open coding analysis was used to examine children’s help-seeking situations. Children’s cognitive, physical, and emotional types of help-seeking situations when using Google and Kids.gov were identified. To explore help features children use and desire when they formulate search queries and evaluate results in Google and Kids.gov, open coding analysis was conducted. Additional descriptive statistics summarized the frequency of help features children used when they formulated search queries and evaluated results in Google and Kids.gov. Finally, this study investigated the effect of children’s domain knowledge on their help seeking and use of help features in using Google and Kids.gov based on linear regression. The level of children’s self-assessed domain knowledge affects occurrences of their help-seeking situations when they formulated search queries in Google. Similarly, children’s domain knowledge quiz scores showed a statistically significant effect on occurrences of their help-seeking situations when they formulated keywords in Google. In the stage of result evaluations, the level of children’s self-assessed domain knowledge influenced their use of help features in Kids.gov. Furthermore, scores of children’s domain knowledge quiz affected their use of help features when they evaluated search results in Kids.gov. Theoretical and practical implications for reducing children’s cognitive, physical, and emotional help-seeking situations when they formulate search queries and evaluate search results in IR systems were discussed based on the results