ACM Computing Surveys (CSUR)
An automated end-to-end lecture capture and broadcasting system
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
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In this paper, a promotion design to improve lending rate of collections in library has been developed. This main ideal of this design is focused on unpopular collections in library with theoretical basis on Long Tail theory proposed by Christ Anderson. According to Long Tail Theory, the unpopular merchandise could be promoted by several strategies such as developing multiple distribution channels for the products, letting the user as a participant in the process of marketing, and customizing the product involved with customer demand. The situation of unpopular collections in library was similar to the merchandise in the tail part of popularity distribution; it could be promoted through reactivate process. This research had analyzed the transaction of the reader lending records to find out the popular books and the unpopular books as the primary step in designing reactive promotion, therefore the process of library data mining could reflect the reader behavior in lending collection of library. Specific readers who had lent the unpopular reading may contribute the improvement in that lending frequency. This research had designed a reactivate promotion to recommend unpopular collections in library by broadcasting readers' interview. The advertisement job is accomplished by Near-Real system which was an e-Learning tool to perform readers' interview around the corners of library.