Exploring features for the automatic identification of user goals in web search

Queries submitted to search engines can be classified according to the user goals into three distinct categories: navigational, informational, and transactional. Such classification may be useful, for instance, as additional information for advertisement selection algorithms and for search engine ranking functions, among other possible applications. This paper presents a study about the impact of using several features extracted from the document collection and query logs on the task of automatically identifying the users’ goals behind their queries. We propose the use of new features not previously reported in literature and study their impact on the quality of the query classification task. Further, we study the impact of each feature on different web collections, showing that the choice of the best set of features may change according to the target collection.     

Generating suggestions for queries in the long tail with an inverted index

This paper proposes an efficient and effective solution to the problem of choosing the queries to suggest to web search engine users in order to help them in rapidly satisfying their information needs. By exploiting a weak function for assessing the similarity between the current query and the knowledge base built from historical users’ sessions, we re-conduct the suggestion generation phase to the processing of a full-text query over an inverted index. The resulting query recommendation technique is very efficient and scalable, and is less affected by the data-sparsity problem than most state-of-the-art proposals. Thus, it is particularly effective in generating suggestions for rare queries occurring in the long tail of the query popularity distribution. The quality of suggestions generated is assessed by evaluating the effectiveness in forecasting the users’ behavior recorded in historical query logs, and on the basis of the results of a reproducible user study conducted on publicly-available, human-assessed data. The experimental evaluation conducted shows that our proposal remarkably outperforms two other state-of-the-art solutions, and that it can generate useful suggestions even for rare and never seen queries.     

Analyzing imbalance among homogeneous index servers in a web search system

The performance of parallel query processing in a cluster of index servers is crucial for modern web search systems. In such a scenario, the response time basically depends on the execution time of the slowest server to generate a partial ranked answer. Previous approaches investigate performance issues in this context using simulation, analytical modeling, experimentation, or a combination of them. Nevertheless, these approaches simply assume balanced execution times among homogeneous servers (by uniformly distributing the document collection among them, for instance)—a scenario that we did not observe in our experimentation. On the contrary, we found that even with a balanced distribution of the document collection among index servers, correlations between the frequency of a term in the query log and the size of its corresponding inverted list lead to imbalances in query execution times at these same servers, because these correlations affect disk caching behavior. Further, the relative sizes of the main memory at each server (with regard to disk space usage) and the number of servers participating in the parallel query processing also affect imbalance of local query execution times. These are relevant findings that have not been reported before and that, we understand, are of interest to the research community.     

Improving the learning of Boolean queries by means of a multiobjective IQBE evolutionary algorithm

The Inductive Query By Example (IQBE) paradigm allows a system to automatically derive queries for a specific Information Retrieval System (IRS). Classic IRSs based on this paradigm [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423–431] generate a single solution (Boolean query) in each run, that with the best fitness value, which is usually based on a weighted combination of the basic performance criteria, precision and recall.     

Examining the effectiveness of real-time query expansion

Interactive query expansion (IQE) (c.f. [Efthimiadis, E. N. (1996). Query expansion. Annual Review of Information Systems and Technology, 31, 121–187]) is a potentially useful technique to help searchers formulate improved query statements, and ultimately retrieve better search results. However, IQE is seldom used in operational settings. Two possible explanations for this are that IQE is generally not integrated into searchers’ established information-seeking behaviors (e.g., examining lists of documents), and it may not be offered at a time in the search when it is needed most (i.e., during the initial query formulation). These challenges can be addressed by coupling IQE more closely with familiar search activities, rather than as a separate functionality that searchers must learn. In this article we introduce and evaluate a variant of IQE known as Real-Time Query Expansion (RTQE). As a searcher enters their query in a text box at the interface, RTQE provides a list of suggested additional query terms, in effect offering query expansion options while the query is formulated. To investigate how the technique is used – and when it may be useful – we conducted a user study comparing three search interfaces: a baseline interface with no query expansion support; an interface that provides expansion options during query entry, and a third interface that provides options after queries have been submitted to a search system. The results show that offering RTQE leads to better quality initial queries, more engagement in the search, and an increase in the uptake of query expansion. However, the results also imply that care must be taken when implementing RTQE interactively. Our findings have broad implications for how IQE should be offered, and form part of our research on the development of techniques to support the increased use of query expansion.     

Children's query types and reformulations in Google search

We investigated the searching behaviors of twenty-four children in grades 6, 7, and 8 (ages 11–13) in finding information on three types of search tasks in Google. Children conducted 72 search sessions and issued 150 queries. Children's phrase- and question-like queries combined were much more prevalent than keyword queries (70% vs. 30%, respectively). Fifty two percent of the queries were reformulations (33 sessions). We classified children's query reformulation types into five classes based on the taxonomy by Liu et al. (2010). We found that most query reformulations were by Substitution and Specialization, and that children hardly repeated queries. We categorized children's queries by task facets and examined the way they expressed these facets in their query formulations and reformulations. Oldest children tended to target the general topic of search tasks in their queries most frequently, whereas younger children expressed one of the two facets more often. We assessed children's achieved task outcomes using the search task outcomes measure we developed. Children were mostly more successful on the fact-finding and fully self-generated task and partially successful on the research-oriented task. Query type, reformulation type, achieved task outcomes, and expressing task facets varied by task type and grade level. There was no significant effect of query length in words or of the number of queries issued on search task outcomes. The study findings have implications for human intervention, digital literacy, search task literacy, as well as for system intervention to support children's query formulation and reformulation during interaction with Google.     

On the use of negation in Boolean IR queries

The negation operator, in various forms in which it appears in Information Retrieval queries, is investigated. The applications include negated terms in Boolean queries, more specifically in the presence of metrical constraints, but also negated characters used in the definition of extended keywords by means of regular expressions. Exact definitions are suggested and their usefulness is shown on several examples. Finally, some implementation issues are discussed, in particular as to the order in which the terms of long queries, with or without negated keywords, should be processed, and efficient heuristics for choosing a good order are suggested.     

Using genetic algorithms to evolve a population of topical queries

Systems for searching the Web based on thematic contexts can be built on top of a conventional search engine and benefit from the huge amount of content as well as from the functionality available through the search engine interface. The quality of the material collected by such systems is highly dependant on the vocabulary used to generate the search queries. In this scenario, selecting good query terms can be seen as an optimization problem where the objective function to be optimized is based on the effectiveness of a query to retrieve relevant material. Some characteristics of this optimization problem are: (1) the high-dimensionality of the search space, where candidate solutions are queries and each term corresponds to a different dimension, (2) the existence of acceptable suboptimal solutions, (3) the possibility of finding multiple solutions, and in many cases (4) the quest for novelty. This article describes optimization techniques based on Genetic Algorithms to evolve “good query terms” in the context of a given topic. The proposed techniques place emphasis on searching for novel material that is related to the search context. We discuss the use of a mutation pool to allow the generation of queries with new terms, study the effect of different mutation rates on the exploration of query-space, and discuss the use of a especially developed fitness function that favors the construction of queries containing novel but related terms.     

Social summarization in collaborative web search

A critical challenge for Web search engines concerns how they present relevant results to searchers. The traditional approach is to produce a ranked list of results with title and summary (snippet) information, and these snippets are usually chosen based on the current query. Snippets play a vital sensemaking role, helping searchers to efficiently make sense of a collection of search results, as well as determine the likely relevance of individual results. Recently researchers have begun to explore how snippets might also be adapted based on searcher preferences as a way to better highlight relevant results to the searcher. In this paper we focus on the role of snippets in collaborative web search and describe a technique for summarizing search results that harnesses the collaborative search behaviour of communities of like-minded searchers to produce snippets that are more focused on the preferences of the searchers. We go on to show how this so-called social summarization technique can generate summaries that are significantly better adapted to searcher preferences and describe a novel personalized search interface that combines result recommendation with social summarization.     

How doctors search: A study of query behaviour and the impact on search results

Professional, workplace searching is different from general searching, because it is typically limited to specific facets and targeted to a single answer. We have developed the semantic component (SC) model, which is a search feature that allows searchers to structure and specify the search to context-specific aspects of the main topic of the documents. We have tested the model in an interactive searching study with family doctors with the purpose to explore doctors’ querying behaviour, how they applied the means for specifying a search, and how these features contributed to the search outcome. In general, the doctors were capable of exploiting system features and search tactics during the searching. Most searchers produced well-structured queries that contained appropriate search facets. When searches failed it was not due to query structure or query length. Failures were mostly caused by the well-known vocabulary problem. The problem was exacerbated by using certain filters as Boolean filters. The best working queries were structured into 2–3 main facets out of 3–5 possible search facets, and expressed with terms reflecting the focal view of the search task. The findings at the same time support and extend previous results about query structure and exhaustivity showing the importance of selecting central search facets and express them from the perspective of search task. The SC model was applied in the highest performing queries except one. The findings suggest that the model might be a helpful feature to structure queries into central, appropriate facets, and in returning highly relevant documents.     

Click data as implicit relevance feedback in web search

Search sessions consist of a person presenting a query to a search engine, followed by that person examining the search results, selecting some of those search results for further review, possibly following some series of hyperlinks, and perhaps backtracking to previously viewed pages in the session. The series of pages selected for viewing in a search session, sometimes called the click data, is intuitively a source of relevance feedback information to the search engine. We are interested in how that relevance feedback can be used to improve the search results quality for all users, not just the current user. For example, the search engine could learn which documents are frequently visited when certain search queries are given.     

The influence of personalization on tag query length in social media search

Social content systems contain enormous collections of unstructured user-generated content, annotated by the collaborative effort of regular Internet users. Tag-clouds have become popular interfaces that allow users to query the database of these systems by clicking relevant terms. However, these single click queries are often not expressive enough to effectively retrieve the desired content. Users have to use multiple clicks or type longer queries to satisfy their information need.     

Analysis of multiple query reformulations on the web: The interactive information retrieval context

This study examines the facets and patterns of multiple Web query reformulations with a focus on reformulation sequences. Based on IR interaction models, it was presumed that query reformulation is the product of the interaction between the user and the IR system. Query reformulation also reflects the interplay between the surface and deeper levels of user interaction. Query logs were collected from a Web search engine through the selection of search sessions in which users submitted six or more unique queries per session. The final data set was composed of 313 search sessions. Three facets of query reformulation (content, format, and resource) as well as nine sub-facets were derived from the data. In addition, analysis of modification sequences identified eight distinct patterns: specified, generalized, parallel, building-block, dynamic, multitasking, recurrent, and format reformulation. Adapting Saracevic’s stratified model, the authors develop a model of Web query reformulation based on the results of the study. The implications for Web search engine design are finally discussed and the functions of an interactive reformulation tool are suggested.     

基于文献多属性测度的新兴主题识别方法研究

对新兴主题的识别有助于科研人员把握相关领域的研究方向。针对目前新兴主题识别以关键词词频判断为主的局限性,构建文献多属性测度模型,利用高关注度、高成长潜力度以及高关联度指标对关键词进行有效识别并筛选,最终确定新兴主题。并以精密单点定位技术为例,说明此方法的实施步骤以及可行性。研究结果表明,根据多属性测度得到的精密单点定位的新兴主题更加明确,指示性强,与该领域现实研究状况(实时精密定点定位、定位误差修正以及模糊度固定等方面)具有较高的吻合度。     

Disambiguated query suggestions and personalized content-similarity and novelty ranking of clustered results to optimize web searches

In this paper, we face the so called “ranked list problem” of Web searches, that occurs when users submit short requests to search engines. Generally, as a consequence of terms’ ambiguity and polysemy, users engage long cycles of query reformulation in an attempt to capture relevant information in the top ranked results.     

Computing controversy: Formal model and algorithms for detecting controversy on Wikipedia and in search queries

Controversy is a complex concept that has been attracting attention of scholars from diverse fields. In the era of Internet and social media, detecting controversy and controversial concepts by the means of automatic methods is especially important. Web searchers could be alerted when the contents they consume are controversial or when they attempt to acquire information on disputed topics. Presenting users with the indications and explanations of the controversy should offer them chance to see the “wider picture” rather than letting them obtain one-sided views. In this work we first introduce a formal model of controversy as the basis of computational approaches to detecting controversial concepts. Then we propose a classification based method for automatic detection of controversial articles and categories in Wikipedia. Next, we demonstrate how to use the obtained results for the estimation of the controversy level of search queries. The proposed method can be incorporated into search engines as a component responsible for detection of queries related to controversial topics. The method is independent of the search engine’s retrieval and search results recommendation algorithms, and is therefore unaffected by a possible filter bubble.Our approach can be also applied in Wikipedia or other knowledge bases for supporting the detection of controversy and content maintenance. Finally, we believe that our results could be useful for social science researchers for understanding the complex nature of controversy and in fostering their studies.     

Effectiveness of additional representations for the search result presentation on the web

The presentation of search results on the web has been dominated by the textual form of document representation. On the other hand, the document’s visual aspects such as the layout, colour scheme, or presence of images have been studied in a limited context with regard to their effectiveness of search result presentation. This article presents a comparative evaluation of textual and visual forms of document representation as additional components of document surrogates. A total of 24 people were recruited for our task-based user study. The experimental results suggest that an increased level of document representation available in the search results can facilitate users’ interaction with a search interface. The results also suggest that the two forms of additional representations are likely beneficial to users’ information searching process in different contexts.     

Information retrieval in the workplace: A comparison of professional search practices

Legal researchers, recruitment professionals, healthcare information professionals, and patent analysts all undertake work tasks where search forms a core part of their duties. In these instances, the search task is often complex and time-consuming and requires specialist expertise to identify relevant documents and insights within large domain-specific repositories and collections. Several studies have been made investigating the search practices of professionals such as these, but few have attempted to directly compare their professional practices and so it remains unclear to what extent insights and approaches from one domain can be applied to another. In this paper we describe the results of a survey of a purposive sample of 108 legal researchers, 64 recruitment professionals and 107 healthcare information professionals. Their responses are compared with results from a previous survey of 81 patent analysts. The survey investigated their search practices and preferences, the types of functionality they value, and their requirements for future information retrieval systems. The results reveal that these professions share many fundamental needs and face similar challenges. In particular a continuing preference to formulate queries as Boolean expressions, the need to manage, organise and re-use search strategies and results and an ambivalence toward the use of relevance ranking. The results stress the importance of recall and coverage for the healthcare and patent professionals, while precision and recency were more important to the legal and recruitment professionals. The results also highlight the need to ensure that search systems give confidence to the professional searcher and so trust, explainability and accountability remains a significant challenge when developing such systems. The findings suggest that translational research between the different areas could benefit professionals across domains.     

Architecture of a grid-enabled Web search engine

Search Engine for South-East Europe (SE4SEE) is a socio-cultural search engine running on the grid infrastructure. It offers a personalized, on-demand, country-specific, category-based Web search facility. The main goal of SE4SEE is to attack the page freshness problem by performing the search on the original pages residing on the Web, rather than on the previously fetched copies as done in the traditional search engines. SE4SEE also aims to obtain high download rates in Web crawling by making use of the geographically distributed nature of the grid. In this work, we present the architectural design issues and implementation details of this search engine. We conduct various experiments to illustrate performance results obtained on a grid infrastructure and justify the use of the search strategy employed in SE4SEE.