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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.
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    Item type:Publication,
    User preference retrieval using semantic categorization for web search
    (2010-05-24)
    Bounoy, Teerakorn
    ;
    Walairacht, Aranya
    Search engines have been one of the most popular ways for people to find web pages of interest. Presently, when a user enters a keyword in a search engine, the search results are usually presented the same result to other users who search the same keyword, which might not be related to each user's field of interest. Therefore, the researcher of this study would like to propose a new searching technique to get each user's most relevant information by using a user preference. This research will categorize user preference to build the user profile and general profile base on user's search history and category hierarchy, respectively. The search engines then use those profiles to determine the interests of each user, execute the search query to obtain a set of relevant documents, and reranking the documents in a manner that best reflects their relevance to the user's profile. Many algorithms have been designed, analyzed, implemented and experimented to find the most appropriate and the most effective one to create the relationship between each keyword and each category to best meet each user's preference.