Information Foraging Theory as a Form of Collective Intelligence for Social Search

Document Type

Book Chapter


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Computer Sciences


The world wide web is growing in size and with the proliferation of large-scale collaborative computing environments Social search has become increasingly important. The focal point of this recent field is to assign relevance and trustworthiness to web-pages by taking into account the reader's perspective rather than web-masters point of view. Current web-searching technologies tend to rely on explicit human recommendations in part because it is hard to obtain user feedback. however these methods are hard to scale. Implicit feedback techniques are a potentially useful alternative. The challenge is in producing implicit web-rankings by reasoning over users' activity during a web-search but without recourse to explicit human intervention. This paper focuses on a novel Social Search formal model based on Information Foraging Theory, showing a diff erent way to implicitly judge web entities by considering effort expended by users in viewing them. 100 university students were recruited to explicitly evaluate the usefulness of 12 thematic web-sites and an experiment was performed implicitly gathering their web-browsing activity. Correlation indexes were adopted and encouraging results where obtained suggesting the existence of a considerable relationship between explicit feedback and implicit derived judgements. Furthermore, a comparison of the results obtained and the results provided by Google was performed. The proposed nature-inspired approach shows that, by considering the same searching query, Social search to be more eff ective than the Google Page-Rank Algorithm. This evidence supports the presentation of a novel general schema for a Social search engine generating implicit web-rankings by taking into account the Collective Intelligence emerged from users by reasoning on their behaviour.

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