Categorization of Computing Education Resources into the ACM Computing Classification System
Yinlin Chen; Paul Bogen; Edward Fox; Haowei Hsieh; Lillian Cassel

The Ensemble Portal harvests resources from multiple heterogonous federated collections. Managing these dynamically increasing collections requires an automatic mechanism to categorize records in to corresponding topics. We propose an approach to use existing ACM DL metadata to build classifiers for harvested resources in the Ensemble project. We also present our experience on utilizing the Amazon Mechanical Turk platform to build ground truth training data sets from Ensemble collections.

Re-ranking Bibliographic Records for Personalized Library Search       
Tadashi Nomoto

This work will introduce a new approach to ranking bibliographic records in library search, which is currently dominated by an OPAC style search paradigm where the system typically presents search results based on not how relevant they are to the query but whether they contain the particular query in verbatim. The approach we propose in the paper provides the user with the ability to access bibliographic records in a way responsive to his or her preferences, which is essentially done by looking at a community or a group of people who share interests with the user and making use of their publication records to re-rank search results. The experiment found that the present approach gives a clear edge over conventional search methods.