What Users See – Structures in Search Engine Results Pages

This paper investigates the composition of search engine results pages. We define what elements the most popular web search engines use on their results pages (e.g., organic results, advertisements, shortcuts) and to which degree they are used for popular vs. rare queries. Therefore, we send 500 queries of both types to the major search engines Google, Yahoo, Live.com and Ask. We count how often the different elements are used by the individual engines. In total, our study is based on 42,758 elements. Findings include that search engines use quite different approaches to results pages composition and therefore, the user gets to see quite different results sets depending on the search engine and search query used. Organic results still play the major role in the results pages, but different shortcuts are of some importance, too. Regarding the frequency of certain host within the results sets, we find that all search engines show Wikipedia results quite often, while other hosts shown depend on the search engine used. Both Google and Yahoo prefer results from their own offerings (such as YouTube or Yahoo Answers). Since we used the .com interfaces of the search engines, results may not be valid for other country-specific interfaces

Dico: a conceptual model to support the design and evaluation of advanced search features for exploratory search

The design of models and tools to support Exploratory Search acquires more importance as the amount of information on the Web grows. The use of advanced search features is a viable approach for query exploration during Exploratory Search. However, the usage of advanced search features remains relatively low since Web search engines became popular, partially because of design decisions that ignore the complex and flexible nature of search activities. In this paper, we introduce Dico: a conceptual model for advanced search features for Exploratory Search, presenting and evaluating a set of guidelines created to support designers and evaluators to design better advanced search features, promoting its usage. Results from an evaluation activity with prospective designers indicated participants were able to make sense of Dico's guidelines, suggesting the guidelines as a promising artifact to support the evaluation of search engines92998710415th IFIP TC.13 International Conference on Human-Computer Interaction (INTERACT

Moving towards adaptive search in digital libraries

Search applications have become very popular over the last two decades, one of the main drivers being the advent of the Web. Nevertheless, searching on the Web is very different to searching on smaller, often more structured collections such as digital libraries, local Web sites, and intranets. One way of helping the searcher locating the right information for a specific information need in such a collection is by providing well-structured domain knowledge to assist query modification and navigation. There are two main challenges which we will both address in this chapter: acquiring the domain knowledge and adapting it automatically to the specific interests of the user community. We will outline how in digital libraries a domain model can automatically be acquired using search engine query logs and how it can be continuously updated using methods resembling ant colony behaviour. © 2011 Springer-Verlag

Deriving query suggestions for site search

Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files. © 2013 ASIS&T

Preference relations based unsupervised rank aggregation for metasearch

Rank aggregation mechanisms have been used in solving problems from various domains such as bioinformatics, natural language processing, information retrieval, etc. Metasearch is one such application where a user gives a query to the metasearch engine, and the metasearch engine forwards the query to multiple individual search engines. Results or rankings returned by these individual search engines are combined using rank aggregation algorithms to produce the final result to be displayed to the user. We identify few aspects that should be kept in mind for designing any rank aggregation algorithms for metasearch. For example, generally equal importance is given to the input rankings while performing the aggregation. However, depending on the indexed set of web pages, features considered for ranking, ranking functions used etc. by the individual search engines, the individual rankings may be of different qualities. So, the aggregation algorithm should give more weight to the better rankings while giving less weight to others. Also, since the aggregation is performed when the user is waiting for response, the operations performed in the algorithm need to be light weight. Moreover, getting supervised data for rank aggregation problem is often difficult. In this paper, we present an unsupervised rank aggregation algorithm that is suitable for metasearch and addresses the aspects mentioned above. We also perform detailed experimental evaluation of the proposed algorithm on four different benchmark datasets having ground truth information. Apart from the unsupervised Kendall-Tau distance measure, several supervised evaluation measures are used for performance comparison. Experimental results demonstrate the efficacy of the proposed algorithm over baseline methods in terms of supervised evaluation metrics. Through these experiments we also show that Kendall-Tau distance metric may not be suitable for evaluating rank aggregation algorithms for metasearch

Use of Negation in Search

Boolean algebra was developed in the 1840s. Since that time, negation, one of the three basic concepts in Boolean algebra, has influenced the fields of information science and information retrieval, particularly in the modern computer era. In Web search engines, one of the present manifestations of information retrieval, little use is being made of this functionality and so little attention is given to it in the literature. This study aims to bolster the understanding of the use and usefulness of negation. Specifically, an Internet search task was developed for which negation was the most appropriate search strategy. This search task was performed by 30 individuals and followed by an interview designed to elicit more information about the participants’ use or non-use of negation during the task. Negation was observed to be used by approximately 17% of users in the study, suggesting that negation may indeed be infrequently used by Internet users. The data obtained during the post-task interview indicate that lack of use of negation stems from users not knowing about negation, having little experience with negation, or simply preferring other methods, even when negation is one of the foremost appropriate methods