Abstract
Intelligent recommendation has been a crucial component in various real-world applications. In job recommendation area, developing an effective and personalized recommendation approach will be very helpful for the job seekers. In order to deliver more accurate job recommendations, we propose a system by incorporating users’ interactions and impressions as the data source and design a hybrid strategy by taking the advantages of three existing one-class collaborative filtering (OCCF) algorithms. The proposed solution combines multi-threading techniques in the traditional item-oriented OCCF (IOCCF) and user-oriented OCCF (UOCCF) algorithms, and also applies an approximation of the sigmoid function in Bayesian personalized ranking (BPR) to improve the efficiency of the overall performance. Based on the experiment results, the proposed system shows the effectiveness of using users’ interactions and impressions by an improvement of 23.78%, 15.61% and 11.50%, and the effectiveness of using the hybrid strategy by a further improvement of 34.55%, 16.20% and 20.90%, over IOCCF, UOCCF and BPR, respectively.
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The implementations of PopRank, IOCCF, UOCCF, BPR and HyOCCF can be downloaded at https://sites.google.com/site/weikep/HyOCCF.zip.
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Acknowledgements
We thank the support of National Natural Science Foundation of China No. 61502307 and No. 61672358, and Natural Science Foundation of Guangdong Province No. 2014A030310268 and No. 2016A030313038.
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Liu, M., Zeng, Z., Pan, W., Peng, X., Shan, Z., Ming, Z. (2017). Hybrid One-Class Collaborative Filtering for Job Recommendation. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2016. Lecture Notes in Computer Science(), vol 10135. Springer, Cham. https://doi.org/10.1007/978-3-319-52015-5_27
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DOI: https://doi.org/10.1007/978-3-319-52015-5_27
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