This work addresses the difficult and underexplored case of supporting users who have very sparse interactions but post informative review texts.
This paper is available on arxiv under CC 4.0 license. Authors: Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de.
Conference on Artificial Intelligence, New York, NY, USA: Association for Computing Machinery, 2017, p. 425–434. Y. Zhang, Q. Ai, X. Chen, and W. B. Croft, “Joint representation learning for top-n recommendation with heterogeneous information sources,” in 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, Singapore, Nov 06 - 10, 2017. ACM, 2017, pp. 1449– 1458. C. Wu, F. Wu, S. Ge, T. Qi, Y. Huang, and X.
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