Abstract
Community based question-and-answer (Q&A) sites rely on well-posed and appropriately tagged questions. However, most platforms have only limited capabilities to support their users in finding the right tags. In this paper, we propose a temporal recommendation model to support users in tagging new questions and thus improve their acceptance in the community. To underline the necessity of temporal awareness of such a model, we first investigate the changes in tag usage and show different types of collective attention in StackOverflow, a community-driven Q&A website for computer programming topics. Furthermore, we examine the changes over time in the correlation between question terms and topics. Our results show that temporal awareness is indeed important for recommending tags in Q&A communities.
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Acknowledgments
This research was funded by the German Research Society, DFG grant no. FOR 1306. We thank the StackOverflow community for sharing the valuable knowledge targeted in this work. Finally, we wish to acknowledge the anonymous reviewers for their detailed and helpful comments to the manuscript.
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Gruetze, T., Krestel, R., Naumann, F. (2016). Topic Shifts in StackOverflow: Ask it Like Socrates. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science(), vol 9612. Springer, Cham. https://doi.org/10.1007/978-3-319-41754-7_18
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DOI: https://doi.org/10.1007/978-3-319-41754-7_18
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