Examining assessor attributes at HARD 2005

The TREC HARD (High accuracy Retrieval from Documents) track was motivated to investigate techniques for personalised retrieval of documents. Through the use of a limited dialogue with the TREC assessors, the track facilitated the gathering and exploitation of information about the assessors' personal search context (e.g. knowledge of search topic) which could be used to improve document retrieval. In this paper we describe experiments, run within the context of the 2005 HARD track, which indicate that assessor attributes such as familiarity, interest and confidence when searching a topic can help determine when the utilisation of automatic query expansion improves retrieval over the original document ranking

University of Strathclyde at TREC HARD

The motivation behind the University of Strathclyde's approach to this years HARD track was inspired from previous experiences by other participants, in particular research by [1], [3] and [4]. A running theme throughout these papers was the underlying hypothesis that a user's familiarity in a topic (i.e. their previous experience searching a subject), will form the basis for what type or style of document they will perceive as relevant. In other words, the user's context with regards to their previous search experience will determine what type of document(s) they wish to retrieve

Investigating User Search Tactic Patterns and System Support in Using Digital Libraries

This study aims to investigate users\u27 search tactic application and system support in using digital libraries. A user study was conducted with sixty digital library users. The study was designed to answer three research questions: 1) How do users engage in a search process by applying different types of search tactics while conducting different search tasks?; 2) How does the system support users to apply different types of search tactics?; 3) How do users\u27 search tactic application and system support for different types of search tactics affect search outputs? Sixty student subjects were recruited from different disciplines in a state research university. Multiple methods were employed to collect data, including questionnaires, transaction logs and think-aloud protocols. Subjects were asked to conduct three different types of search tasks, namely, known-item search, specific information search and exploratory search, using Library of Congress Digital Libraries. To explore users\u27 search tactic patterns (RQ1), quantitative analysis was conducted, including descriptive statistics, kernel regression, transition analysis, and clustering analysis. Types of system support were explored by analyzing system features for search tactic application. In addition, users\u27 perceived system support, difficulty, and satisfaction with search tactic application were measured using post-search questionnaires (RQ2). Finally, the study examined the causal relationships between search process and search outputs (RQ 3) based on multiple regression and structural equation modeling. This study uncovers unique behavior of users\u27 search tactic application and corresponding system support in the context of digital libraries. First, search tactic selections, changes, and transitions were explored in different task situations - known-item search, specific information search, and exploratory search. Search tactic application patterns differed by task type. In known-item search tasks, users preferred to apply search query creation and following search result evaluation tactics, but less query reformulation or iterative tactic loops were observed. In specific information search tasks, iterative search result evaluation strategies were dominantly used. In exploratory tasks, browsing tactics were frequently selected as well as search result evaluation tactics. Second, this study identified different types of system support for search tactic application. System support, difficulty, and satisfaction were measure in terms of search tactic application focusing on search process. Users perceived relatively high system support for accessing and browsing tactics while less support for query reformulation and item evaluation tactics. Third, the effects of search tactic selections and system support on search outputs were examined based on multiple regression. In known-item searches, frequencies of query creation and accessing forwarding tactics would positively affect search efficiency. In specific information searches, time spent on applying search result evaluation tactics would have a positive impact on success rate. In exploratory searches, browsing tactics turned out to be positively associated with aspectual recall and satisfaction with search results. Based on the findings, the author discussed unique patterns of users\u27 search tactic application as well as system design implications in digital library environments

A machine learning-based approach to predicting success of questions on social question-answering

While social question-answering (SQA) services are becoming increasingly popular, there is often an issue of unsatisfactory or missing information for a question posed by an information seeker. This study creates a model to predict question failure, or a question that does not receive an answer, within the social Q&A site Yahoo! Answers. To do so, observed shared characteristics of failed questions were translated into empirical features, both textual and non-textual in nature, and measured using machine extraction methods. A classifier was then trained using these features and tested on a data set of 400 questions – half of them successful, half not – to determine the accuracy of the classifier in identifying failed questions. The results show the substantial ability of the approach to correctly identify the likelihood of success or failure of a question, resulting in a promising tool to automatically identify ill-formed questions and/or questions that are likely to fail and make suggestions on how to revise them.published or submitted for publicationis peer reviewe

History Of Search Engines

As the number of sites on the Web increased in the mid-to-late 90s, search engines started appearing to help people find information quickly. Search engines developed business models to finance their services, such as pay per click programs offered by Open Text in 1996 and then Goto.com in 1998. Goto.com later changed its name to Overture in 2001, and was purchased by Yahoo! in 2003, and now offers paid search opportunities for advertisers through Yahoo! Search Marketing. Google also began to offer advertisements on search results pages in 2000 through the Google Ad Words program. By 2007, pay-per-click programs proved to be primary money-makers for search engines. In a market dominated by Google, in 2009 Yahoo! and Microsoft announced the intention to forge an alliance. The Yahoo! & Microsoft Search Alliance eventually received approval from regulators in the US and Europe in February 2010. Search engine optimization consultants expanded their offerings to help businesses learn about and use the advertising opportunities offered by search engines, and new agencies focusing primarily upon marketing and advertising through search engines emerged. The term "Search Engine Marketing" was proposed by Danny Sullivan in 2001 to cover the spectrum of activities involved in performing SEO, managing paid listings at the search engines, submitting sites to directories, and developing online marketing strategies for businesses, organizations, and individuals. Some of the latest theoretical advances include Search Engine Marketing Management (SEMM). SEMM relates to activities including SEO but focuses on return on investment (ROI) management instead of relevant traffic building (as is the case of mainstream SEO). SEMM also integrates organic SEO, trying to achieve top ranking without using paid means of achieving top in search engines, and PayPerClick SEO. For example some of the attention is placed on the web page layout design and how content and information is displayed to the website visitor