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Author: search.filters.author.Azzopardi, Leif

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    Item type:Publication,
    Crowdsourcing interactions: Using crowdsourcing for evaluating interactive information retrieval systems
    (2013-04-01)
    Zuccon, Guido
    ;
    Leelanupab, Teerapong
    ;
    Whiting, Stewart
    ;
    Yilmaz, Emine
    ;
    Jose, Joemon M.
    In the field of information retrieval (IR), researchers and practitioners are often faced with a demand for valid approaches to evaluate the performance of retrieval systems. The Cranfield experiment paradigm has been dominant for the in-vitro evaluation of IR systems. Alternative to this paradigm, laboratory-based user studies have been widely used to evaluate interactive information retrieval (IIR) systems, and at the same time investigate users' information searching behaviours. Major drawbacks of laboratory-based user studies for evaluating IIR systems include the high monetary and temporal costs involved in setting up and running those experiments, the lack of heterogeneity amongst the user population and the limited scale of the experiments, which usually involve a relatively restricted set of users. In this paper, we propose an alternative experimental methodology to laboratory-based user studies. Our novel experimental methodology uses a crowdsourcing platform as a means of engaging study participants. Through crowdsourcing, our experimental methodology can capture user interactions and searching behaviours at a lower cost, with more data, and within a shorter period than traditional laboratory-based user studies, and therefore can be used to assess the performances of IIR systems. In this article, we show the characteristic differences of our approach with respect to traditional IIR experimental and evaluation procedures. We also perform a use case study comparing crowdsourcing-based evaluation with laboratory-based evaluation of IIR systems, which can serve as a tutorial for setting up crowdsourcing-based IIR evaluations. © 2012 Springer Science+Business Media, LLC.
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    Item type:Publication,
    Indexing without spam
    (2011-12-01)
    Zuccon, Guido
    ;
    Leelanupab, Teerapong
    ;
    Nguyen, Anthony
    ;
    Azzopardi, Leif
    The presence of spam in a document ranking is a major issue for Web search engines. Common approaches that cope with spam remove from the document rankings those pages that are likely to contain spam. These approaches are implemented as post-retrieval processes, that filter out spam pages only after documents have been retrieved with respect to a user's query. In this paper we propose removing spam pages at indexing time, therefore obtaining a pruned index that is virtually "spam-free". We investigate the benefits of this approach from three points of view: indexing time, index size, and retrieval performance. Not surprisingly, we found that the strategy decreases both the time required by the indexing process and the space required for storing the index. Surprisingly instead, we found that by considering a spam-pruned version of a collection's index, no difference in retrieval performance is found when compared to that obtained by traditional post-retrieval spam filtering approaches.