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Influences on Query Reformalution in Collaborative Web Search

Zhen Yue, Shuguang Han, Daqing He, and Jiepu Jiang ( Computer-2014 ) http://ieeexplore.ieee.org/abstract/document/6766167/ In the paper “Influences on Query Reformation in Collaborative Web Search”, Yue et al. investigate a study to analyze the influences of collaboration between members in a group for information seeking. They are motivated to study (1) the influence of collaboration on search query formulation and reformulation; (2) similarities and differences exist in terms of query reformulation for collaborative searches compared with individual searches; and (3) the nature of the query task influence query reformulation in collaborative information seeking (CIS). In order to run the study, Yue et al. used CollabSearch, a Web search system developed by their group for collaborative users. The system includes three frames: a topic statement page which shows the description about the search task a group want to conduct; a web search page which displays Google search results
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Personalized Access to Scientific Publications: from Recommendation to Explanation

Dario De Nart, Felice Ferrara, and Carlo Tasso ( UMAP-2013 ) http://link.springer.com/chapter/10.1007/978-3-642-38844-6_26 In the paper “Personalized Access to Scientific Publications: from Recommendation to Explanation”, Dario De Nart et al. present a Recommender and Explanation System (RES) that is able to recommend scientific publication and show explanation to users.  In order to do recommendation, Dario De Nart et al., firstly, use the The Dikpe Keyphrase extraction algorithm introduced by Pudota (2010) to extract keyphrases from papers. Each keyphrase has a weight called keyphraseness that reveals the several lexical and statistical indicators exploited in the extraction process. Higher is the keyphraseness, more relevant is the KP. For each paper in the collection, they represent it as a conceptual graph. Each node in the graph is a term broken down from a KP. Two nodes are connected when they appear in the same KP. For instance, the KP “document retrieval” is split into tw
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FolkTrails: Interpreting Navigation Behavior in a Social Tagging System

Thomas Niebler, Martin Becker, Daniel Zoller, Stephan Doerfel, & Andreas Hotho ( CIKM-2016 ) http://dl.acm.org/citation.cfm?doid=2983323.2983686 In the paper “FolkTrails: Interpreting Navigation Behavior in a Social Tagging System”, Niebler et al. present an investigate navigation trails in the popular scholarly social tagging system BibSonomy. They tried to understand users’ dynamic browsing behaviors on this kind of systems, how individual users behave differently on navigation, and whether the semantic nature of the underlying folksonomy can help to explain navigation. Firstly, Niebler et al. study different hypotheses about the navigational user behavior in Bibsonomy, a social bookmarking system. They test these hypotheses by using HypTrails, an approach utilizing first-order Markov chain models and Bayesian inference for expressing and comparing hypotheses about human trails. These hypotheses include: Uniform hypothesis: serves as a baseline, users randomly pick a l
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Personalized Social Search Based on the User’s Social Network

David Carmel et al. - IBM Research Lab in Haifa, Israel ( CIKM-2009 ) http://dl.acm.org/citation.cfm?doid=1645953.1646109 Personalizing the search process considers the searcher’s personal attributes & preferences so that the system is able to, for example, do personalized query expansion, re-rank the result list or filter presented information. However, few studies consider the preferences of the user’s related people in social network which can be utilized to enrich the user’s preferences. In the paper “Personalized Social Search Based on the User’s Social Network”, Carmel et al. presents a study that leverages both aforementioned information resources to improve social search, which is the search process over “social” data (e.g., documents or people) gathered from Web 2.0 applications. Particularly, they plan to re-rank search results by considering the relationships with other users in the searcher’s social network (SN). Firstly, Carmel et al. build user profiles from t
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