Abstract
The problem of the previous researches on One Class Collaborative Filtering (OCCF) is that they focused on either rating prediction or ranking prediction, no concerted research effort has been devoted to developing recommendation approach that simultaneously optimize both ratings and rank of the recommended items. In order to solve this problem, a new unified OCCF approach (UOCCF) based on Probabilistic Matrix Factorization (PMF) approach and the newest Collaborative Less-is-More Filtering (CLiMF) approach was proposed. Experimental results on practical dataset showed that our proposed UOCCF approach outperformed existing OCCF approaches over different evaluation metrics.
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Acknowledgments
This work is sponsored in part by the National Natural Science Foundation of China (No. 61370186), Natural Science Foundation of Guangdong Province (2016A030310018), Science and Technology Planning Project of Guangdong Province (No. 2014A010103040, No. 2014B010116001, No. 2015A020209178, No. 2016A030303058), Science and Technology Planning Project of Guangzhou (No. 201604010049, No. 201510010203), Appropriative Researching Fund for Professors and Doctors, Guangdong University of Education under Grant (No. 2015ARF25), Second batch open subject of mechanical and electrical professional group engineering technology development center in Foshan city (No. 2015-KJZX139), College Students’ Science and Technology Innovation fund of Guangdong Province (No. G2016Z08).
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Li, G., He, Cb., Wang, L., Pan, Jc., Chen, Q., Li, L. (2016). An Unified One Class Collaborative Filtering Algorithm. In: Li, Y., Xiang, G., Lin, H., Wang, M. (eds) Social Media Processing. SMP 2016. Communications in Computer and Information Science, vol 669. Springer, Singapore. https://doi.org/10.1007/978-981-10-2993-6_23
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DOI: https://doi.org/10.1007/978-981-10-2993-6_23
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