Abstract
PurposeAs a relatively new computing paradigm, crowdsourcing has gained enormous attention in the recent decade. Its compliance with the Web 2.0 principles, also, puts forward unprecedented opportunities to empower the related services and mechanisms by leveraging humans’ intelligence and problem solving abilities. With respect to the pivotal role of search engines in the Web and information community, this paper aims to investigate the advantages and challenges of incorporating people – as intelligent agents – into search engines’ workflow.Design/methodology/approachTo emphasize the role of the human in computational processes, some specific and related areas are studied. Then, through studying the current trends in the field of crowd-powered search engines and analyzing the actual needs and requirements, the perspectives and challenges are discussed.FindingsAs the research on this topic is still in its infancy, it is believed that this study can be considered as a roadmap for future works in the field. In this regard, current status and development trends are delineated through providing a general overview of the literature. Moreover, several recommendations for extending the applicability and efficiency of next generation of crowd-powered search engines are presented. In fact, becoming aware of different aspects and challenges of constructing search engines of this kind can shed light on the way of developing working systems with respect to essential considerations.Originality/valueThe present study was aimed to portrait the big picture of crowd-powered search engines and possible challenges and issues. As one of the early works that provided a comprehensive report on different aspects of the topic, it can be regarded as a reference point.
Highlights
The days of relying only on machines for performing computing tasks and problem solving have gone
How does crowdsourcing help Web search? Humans’ power – according to the context and applications – can be leveraged in many different ways from providing training data for the machine (Kairam and Heer, 2016; Chang et al, 2017) to collaborating with an algorithm to provide more precise outputs (Fan et al, 2014; Sarma et al, 2014), e.g. in the form of a quality controller or supervisor. When it comes to the Web search, crowdsourcing is mainly related to improving underlying processes or providing users with some assistance on finding more relevant answers
It is expected to obtain different Search engines answers when issuing same search query in different search engines. In this regard, taking advantages of different search engines and information retrieval systems to provide the user with most relevant answers can be an interesting and working strategy
Summary
The days of relying only on machines for performing computing tasks and problem solving have gone. In other words, demystifying users’ intention of search (terms), finding most relevant matches and ranking the results so that best conform to users’ goals and preferences cannot be achieved by relying only on machines’ intelligence and capabilities In this regard, so-called crowd(human)-powered search engines (Parameswaran et al, 2014) have gained momentum. Humans’ power – according to the context and applications – can be leveraged in many different ways from providing training data for the machine (Kairam and Heer, 2016; Chang et al, 2017) to collaborating with an algorithm to provide more precise outputs (Fan et al, 2014; Sarma et al, 2014), e.g. in the form of a quality controller or supervisor When it comes to the Web search, crowdsourcing is mainly related to improving underlying processes or providing users (searchers) with some assistance on finding more relevant answers. In this regard, taking advantages of different search engines and information retrieval systems to provide the user with most relevant answers can be an interesting and working strategy
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