Abstract
Collaborative Filtering is a very popular method in recommendation systems. In item recommendation tasks, a list of items is recommended to users by ranking, but traditional CF methods do not treat it as a ranking problem for implicit feedback datasets. In this paper, we propose MAP-kNN, an adaptive kNN approach using listwise approach for implicit feedback datasets. The similarity matrix is learned by maximizing the Mean Average Precision, which is a well-known measurement in information retrieval for representing the performance of a list of ranked items. An optimization strategy and a new sampling method are proposed to improve the learning efficiency of MAP-kNN. The complexity of our algorithm over each iteration after optimization is lower than other methods that also use listwise approach. Experimental results on two datasets indicate that our approach outperforms other state of the art recommendation approaches.
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Acknowledgement
This work was partially supported by the National Natural Science Foundation of China (NSFC) projects No. 61202296, No. 61370229, No. 61370178, the S&T Projects of Guangdong Province No. 2013B090800024, No. 2014B010103004, No. 2014B010117007, No. 2015A030401087, No. 2015B010110002, GDUPS(2015), the Natural Science Foundation of Guangdong Province project No. S2012030006242 and the Science and Technology Program of Guangzhou project No. 201508010067.
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Wu, BX., Xiao, J., Zhu, J., Ding, C. (2016). An Adaptive kNN Using Listwise Approach for Implicit Feedback. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_42
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DOI: https://doi.org/10.1007/978-3-319-45814-4_42
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