Mining user tasks from print logs

Xin LI, Lei ZHANG, Ping LUO, Enhong CHEN, Guandong XU, Yu ZONG, Chu GUAN

Research output: Chapter in Book/Report/Conference proceedingChapters

3 Citations (Scopus)


With lots of applications emerging in World Wide Web, many interaction data from users are collected and exploited to discover user behavior or interest patterns. In this paper, we attempt to exploit a new interaction data, namely print logs, where each record is printing URLs selected by a user using a popular web printing tool. Users usually print web contents based on an intention (subtask or task). Apparently, mining common print tasks from print logs is able to capture users' intentions, which undoubtedly benefits many web applications, such as task oriented recommendation and behavior targeting. However, it is not an easy job to perform this due to the difficulty of URL topic representation and task formulation. To this end, we propose a general framework, named UPT (Users Print Tasks mining framework), for mining print tasks from print logs. Specifically, we attempt to leverage delicious (a social book marking web service) as an external thesaurus to expand the expression of each URL by selecting tags associated with the domain of each URL. Then, we construct a tag co-occurrence graph where similar tags can be clustered as subtasks. If we view each subtask as an item, then the print log is transformed to a transaction database, on which an efficient pattern mining algorithm is proposed to induce tasks. Finally, we evaluate the effectiveness of the proposed framework through experiments on a real print log. Copyright © 2014 IEEE.

Original languageEnglish
Title of host publicationProceedings of 2014 International Joint Conference on Neural Networks, IJCNN
ISBN (Electronic)9781479914845
Publication statusPublished - Sept 2014


Li, X., Zhang, L., Luo, P., Chen, E., Xu, G., Zong, Y., & Guan, C. (2014). Mining user tasks from print logs. In Proceedings of 2014 International Joint Conference on Neural Networks, IJCNN (pp. 1250-1257). IEEE.


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