Mining the search trails of surfing crowds: identifying relevant websites from user activity Export

@inproceedings{citeulike:2775006, address = {New York, NY, USA}, author = {Bilenko, Mikhail and White, Ryen W.}, booktitle = {WWW '08: Proceeding of the 17th international conference on World Wide Web}, citeulike-article-id = {2775006}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1367497.1367505}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1367497.1367505}, comment = {Studies how to exploit information in search trails to improve the ranking of a search engine. "Comprehensive logs of post-search behavior are an informative source of implicit feedback for inferring resource relevance". "over 60\% of the queries are unique to a given user session over the twelve-month dataset at our disposal." -- this is why they expand using query terms. Dwelling times are important. Evaluates accuracy at the site level. Full trail is better than first click or last click. Log(dwell time) is better than dwell time or number of clicks. "Access to large datasets of user behavior is essential for obtaining good performance" -- they use 140M trails obtained over a year, and the performance of their algorithms starts to plateau at around 80M trails.}, doi = {10.1145/1367497.1367505}, isbn = {9781605580852}, keywords = {clicks, ranking, social}, pages = {51--60}, posted-at = {2008-06-25 09:07:22}, priority = {0}, publisher = {ACM}, title = {Mining the search trails of surfing crowds: identifying relevant websites from user activity}, url = {http://dx.doi.org/10.1145/1367497.1367505}, year = {2008} }

TY - CONF ID - citeulike:2775006 L3 - citeulike-article-id:2775006 SN - 9781605580852 AD - New York, NY, USA EP - 60 TI - Mining the search trails of surfing crowds: identifying relevant websites from user activity PB - ACM T2 - WWW '08: Proceeding of the 17th international conference on World Wide Web SP - 51 KW - clicks KW - ranking KW - social N1 - Studies how to exploit information in search trails to improve the ranking of a search engine. "Comprehensive logs of post-search behavior are an informative source of implicit feedback for inferring resource relevance". "over 60% of the queries are unique to a given user session over the twelve-month dataset at our disposal." -- this is why they expand using query terms. Dwelling times are important. Evaluates accuracy at the site level. Full trail is better than first click or last click. Log(dwell time) is better than dwell time or number of clicks. "Access to large datasets of user behavior is essential for obtaining good performance" -- they use 140M trails obtained over a year, and the performance of their algorithms starts to plateau at around 80M trails. AU - Bilenko, Mikhail AU - White, Ryen PY - 2008/// UR - http://dx.doi.org/10.1145/1367497.1367505 ER -