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Social Ranking: Finding Relevant Content in Web 2.0 (2008)
| Venue: | In Proceedings of International Workshop on Recommender Systems |
| Citations: | 6 - 1 self |
BibTeX
@INPROCEEDINGS{Zanardi08socialranking:,
author = {Valentina Zanardi and Licia Capra},
title = {Social Ranking: Finding Relevant Content in Web 2.0},
booktitle = {In Proceedings of International Workshop on Recommender Systems},
year = {2008}
}
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Abstract
Abstract. Social (or folksonomic) tagging has become a very popular way to describe, categorise, search, discover and navigate content within Web 2.0 websites. Unlike taxonomies, which overimpose a hierarchical categorisation of content, folksonomies empower end users by enabling them to freely create and choose the categories (in this case, tags) that best describe some content. However, as tags are informally defined, continually changing, and ungoverned, social tagging has often been criticised for lowering, rather than increasing, the efficiency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of users and the noise they introduce. In this paper, we propose a method to increase the efficiency of searches within Web 2.0 that is grounded on recommender system techniques. We measure users ’ similarity based on their past tag activity. We infer tags ’ relationships based on their association to content. We then propose a mechanism to answer a user’s query that ranks (recommends) content based on the inferred semantic distance of the query to the tags associated to such content, weighted by the similarity of the querying user to the users who created those tags. We evaluate the effectiveness of this mechanism when performing searches on the CiteULike dataset. 1
