Assessing geographic relevance for mobile search: A computational model and its validation via crowdsourcing

Tumasch Reichenbacher, Stefano De Sabbata, Ross S. Purves, Sara I. Fabrikant
2016 Journal of the Association for Information Science and Technology  
relevance for mobile search: A computational model and its validation via crowdsourcing. Abstract The selection and retrieval of relevant information from the information universe on the web is becoming increasingly important in addressing information overload. It has also been recognised that geography is an important criterion of relevance, leading to the research area of geographic information retrieval. As users increasingly retrieve information in mobile situations relevance is often
more » ... d to geographic features in the real world as well as their representation in web documents. We present two methods for assessing geographic relevance (GR) of geographic entities in a mobile use context that include the five criteria topicality, spatiotemporal proximity, directionality, cluster, and co-location. To determine the effectiveness and validity of these methods, we evaluate them through a user study conducted on the Amazon Mechanical Turk crowdsourcing platform. An analysis of relevance ranks for geographic entities in three scenarios produced by two GR methods, two baseline methods, and human judgements collected in the experiment reveal that one of the GR methods produces similar ranks as human assessors.
doi:10.1002/asi.23625 fatcat:aps4go2jfzehfo43oqdvektouq