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Tag Data and Personalized Information Retrieval

Tag Data and Personalized Information Retrieval. Abstract. This work investigate the use of tag data for evaluating personalized retrieval systems involving thousands of users . They demonstrate how one can rate the quality of personalized retrieval results.

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Tag Data and Personalized Information Retrieval

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  1. Tag Data and Personalized Information Retrieval

  2. Abstract • This work investigate the use of tag data for evaluating personalized retrieval systems involving thousands of users. • They demonstrate how one can rate the quality of personalized retrieval results. • They show that a user's “bookmark history" can be used to improve search results via personalization.

  3. Introduction (1/4) • Researchers require personalized relevance judgments to evaluate their systems. • Documents are deemed relevant for a particular query by a particular individual. • An excellent source of such information is personal query logsand click-through data , Z. Dou (2007). • Query logs are not readily available to the wider research community due primarily to privacy and monetary concerns. • The standard test collection in IR, namely the TREC datasets, cannot be used for evaluating personalized IR systems. • The topics (queries) and corresponding relevance judgments are not associated with particular users. • One public source of personalized ratings is tag data.

  4. Introduction (2/4) Tag Data • Social bookmarking systems such as del.icio.us, StumbleUponand Bibsonomyare a recent and popular phenomenon. • In these systems, users label interesting web pages with tags. • These sites oer an alternative model for discovering information online. • Users can browse tags for popular pages that have been tagged by a number of different users. • These systems can be seen to provide consensus categorizations of interesting websites.

  5. Introduction (3/4) • Various researchers have investigated the applicability of social bookmarking data to improve Web search results, S. Bao (2007), Y. Yanbe (2007). • Heymann(2008) found that the bookmark data had a good coverage of interesting pages on the Web. • Bookmarked URLs were disproportionately common in search results given the small relative size of the del.icio.us index. • Over a set of 30,000 popular queries, they found that 19% of the top 10 results. • 9% of the top 100 results were present in the index.

  6. Introduction (4/4) • Whether social bookmarking data can be used to improve Web search from the perspective of personalization? • Can tag data be used to approximate actual user queries to a search engine? • How can we evaluate personalized IR systems using information contained in social bookmarks (tag data)? • Is there enough information in the tags/bookmarks in a user's history in order to build a profile of the user that will be useful for personalizing search engine results?

  7. Related Work

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