Random walks on the click graph Export

@inproceedings{citeulike:1542207, abstract = {Search engines can record which documents were clicked for which query, and use these query-document pairs as "soft" relevance judgments. However, compared to the true judgments, click logs give noisy and sparse relevance information. We apply a Markov random walk model to a large click log, producing a probabilistic ranking of documents for a given query. A key advantage of the model is its ability to retrieve relevant documents that have not yet been clicked for that query and rank those effectively. We conduct experiments on click logs from image search, comparing our ("backward") random walk model to a different ("forward") random walk, varying parameters such as walk length and self-transition probability. The most effective combination is a long backward walk with high self-transition probability.}, address = {New York, NY, USA}, author = {Craswell, Nick and Szummer, Martin}, booktitle = {SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval}, citeulike-article-id = {1542207}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1277741.1277784}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1277741.1277784}, doi = {10.1145/1277741.1277784}, isbn = {978-1-59593-597-7}, keywords = {click-graph, pagerank, query-logs, random-walk, yrbcn}, location = {Amsterdam, The Netherlands}, pages = {239--246}, posted-at = {2008-05-16 15:13:10}, priority = {2}, publisher = {ACM}, title = {Random walks on the click graph}, url = {http://dx.doi.org/10.1145/1277741.1277784}, year = {2007} }

TY - CONF ID - citeulike:1542207 L3 - citeulike-article-id:1542207 N2 - Search engines can record which documents were clicked for which query, and use these query-document pairs as "soft" relevance judgments. However, compared to the true judgments, click logs give noisy and sparse relevance information. We apply a Markov random walk model to a large click log, producing a probabilistic ranking of documents for a given query. A key advantage of the model is its ability to retrieve relevant documents that have not yet been clicked for that query and rank those effectively. We conduct experiments on click logs from image search, comparing our ("backward") random walk model to a different ("forward") random walk, varying parameters such as walk length and self-transition probability. The most effective combination is a long backward walk with high self-transition probability. CY - Amsterdam, The Netherlands SN - 978-1-59593-597-7 AD - New York, NY, USA EP - 246 TI - Random walks on the click graph PB - ACM T2 - SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval SP - 239 KW - click-graph KW - pagerank KW - query-logs KW - random-walk KW - yrbcn AU - Craswell, Nick AU - Szummer, Martin PY - 2007/// UR - http://dx.doi.org/10.1145/1277741.1277784 ER -