%0 Conference Paper %1 karweg2011evolving %A Karweg, Bastian %A Huetter, Christian %A Böhm, Klemens %B Proceedings of the 20th ACM international conference on Information and knowledge management %C New York, NY, USA %D 2011 %I ACM %K social-search tagging toread %P 1825--1834 %R 10.1145/2063576.2063839 %T Evolving social search based on bookmarks and status messages from social networks %U http://doi.acm.org/10.1145/2063576.2063839 %X Social search is a variant of information retrieval where a document or website is considered relevant if individuals from the searcher's social network have interacted with it. Our ranking metric Social Relevance Score (SRS) is based on two factors. First, the engagement intensity quantifies the effort a user has made during an interaction. Second, users can assign a trust score to each person from their social network, which is then refined using social network analysis. We have tested our hypotheses with our search engine www.social-search.com, which extends the existing social bookmarking platform folkd.com. Our search engine integrates information the folkd.com users share through the popular social networks Twitter and Facebook. With permission of 2,385 testers, we have connected to their social graphs to generate a large-scale real-world dataset. Over the course of a two-month field study, 468,889 individuals have generated 24,854,281 website recommendations. We have used those links to enhance their search results while measuring the impact on the search behavior. We have found that social results are available for most queries and usually lead to more satisfying results. %@ 978-1-4503-0717-8

@inproceedings{karweg2011evolving, abstract = {Social search is a variant of information retrieval where a document or website is considered relevant if individuals from the searcher's social network have interacted with it. Our ranking metric Social Relevance Score (SRS) is based on two factors. First, the engagement intensity quantifies the effort a user has made during an interaction. Second, users can assign a trust score to each person from their social network, which is then refined using social network analysis. We have tested our hypotheses with our search engine www.social-search.com, which extends the existing social bookmarking platform folkd.com. Our search engine integrates information the folkd.com users share through the popular social networks Twitter and Facebook. With permission of 2,385 testers, we have connected to their social graphs to generate a large-scale real-world dataset. Over the course of a two-month field study, 468,889 individuals have generated 24,854,281 website recommendations. We have used those links to enhance their search results while measuring the impact on the search behavior. We have found that social results are available for most queries and usually lead to more satisfying results.}, acmid = {2063839}, added-at = {2012-02-08T22:27:32.000+0100}, address = {New York, NY, USA}, author = {Karweg, Bastian and Huetter, Christian and B\"{o}hm, Klemens}, biburl = {https://www.bibsonomy.org/bibtex/29af70b2c55171a55c5a25d1bef51108f/beate}, booktitle = {Proceedings of the 20th ACM international conference on Information and knowledge management}, description = {Evolving social search based on bookmarks and status messages from social networks}, doi = {10.1145/2063576.2063839}, interhash = {0d138a65e141e0901620a05b561cba87}, intrahash = {9af70b2c55171a55c5a25d1bef51108f}, isbn = {978-1-4503-0717-8}, keywords = {social-search tagging toread}, location = {Glasgow, Scotland, UK}, numpages = {10}, pages = {1825--1834}, publisher = {ACM}, series = {CIKM '11}, timestamp = {2012-02-08T22:27:32.000+0100}, title = {Evolving social search based on bookmarks and status messages from social networks}, url = {http://doi.acm.org/10.1145/2063576.2063839}, year = 2011 }