Information Foraging Theory as a Form of Collective Intelligence for Social Search
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 different 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 effective 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.
KeywordsCollective Intelligence Implicit Feedback Explicit Feedback Forage Theory Explicit Judgement
Unable to display preview. Download preview PDF.
- 1.Stephens, D.W., Krebs, J.R.: Foraging Theory, Princeton, NJ (1986)Google Scholar
- 4.Miller, C.S., Remington, R.W.: Modeling Information Navigation: implications for Information Architecture. In: HCI (2004)Google Scholar
- 5.Kitajima, M., Blackmon, M.H., Polson, P.G.: Cognitive Architecture for Website Design and Usability evaluation: Comprehension and Information Scent in Performing by Exploration. In: HCI, Las Vegas (2005)Google Scholar
- 6.Longo, L., Barrett, S., Dondio, P.: Toward Social Search: from Explicit to Implicit Collaboration to Predict Users’ Interests. In: WEBIST 2009 (2009)Google Scholar
- 7.Kelly, D., et al.: Reading Time, Scrolling and Interaction: exploring Implicit Sources of User Preferences for Relevance Feedback During Interactive Information Retrieval. In: SIGIR 2001, New Orleans, USA (2001)Google Scholar
- 8.Agichtein, E., Brill, E., Dumais, S.: Improving Web Search Ranking by Incorporating User Behavior Information. In: SIGIR 2006, Seattle, USA (2006)Google Scholar
- 9.Agichtein, E., Zheng, Z.: Identifying Best Bet Web Search Results by Mining Past User Behavior. In: Kdd 2006, Philadelphia, Pennsylvaia, USA (2006)Google Scholar
- 10.Atterer, R., et al.: Knowing the User’s Every Move - User Activity Tracking for Website Usability Evaluation and Implicit Interaction. In: WWW 2006, Edinburgh, May 23-26 (2006)Google Scholar
- 11.Velayathan, G., Yamada, S.: Behavior-based Web Page Evaluation. In: WWW 2007, Banff, Alberta, Canada, May 8-12 (2007)Google Scholar
- 12.Morita, M., Shinoda, Y.: Information Filtering Based on User Behavior analysis and Best Match Text Retrieval. In: 17th ACM SIGIR (1996)Google Scholar
- 13.Longo, L., Dondio, P., Barrett, S.: Temporal Factors to evaluate trustworthiness of virtual identities. In: IEEE, SECURECOMM 2007, France (2007)Google Scholar