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Multi-level preference regression for cold-start recommendations

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Abstract

Due to the absence of historical ratings of new users/items, cold-start recommendation remains a challenge for collaborative filtering. Many matrix factorization based methods are used to predict new user’s/item’s latent profile before predicting ratings. This kind of methods is usually non-convex. In this work, we design a new convex framework for cold-start recommendations, multi-level preference regression (MPR), directly to predict the ratings rather than latent profiles. We suppose that ratings are mainly affected by three components: (1) correlation between user’s attributes (such as age and gender) and item’s attributes (such as genre and producer); (2) each user’s preference on item’s attributes; (3) item’s popularity in a group of users with some attributes. Adjusting the impact of the three components, we can tackle three cold-start scenarios of user, item, and system. In the MPR framework, three different learning strategies are discussed: pointwise regression, pairwise regression, and large-margin learning. Experimental results on three datasets demonstrate that the proposed model can achieve the state of the art in the user cold-start scenario and the best performance in other scenarios.

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Notes

  1. http://www.amazon.com.

  2. https://www.netflix.com.

  3. https://www.taobao.com.

  4. https://www.facebook.com.

  5. http://www2.informatik.uni-freiburg.de/~cziegler/BX/.

  6. http://grouplens.org/datasets/movielens/.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (NSFC) (Nos. 61233011 and 61672332), by Jiangsu Natural Science Foundation (No. BK20131351), by Science Foundation for The Excellent Youth Scholars of Ministry of Education of China (No. 61322211), by Program for the Innovative Talents of Higher Learning Institutions of Shanxi (No. 02150116072021), and Shanjin Scholars Program.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China

    Furong Peng, Chao Ma, Jianfeng Lu & Jingyu Yang

  2. Department of Electronic and Information Engineering, Shanxi University, Taiyuan, 030013, Shanxi, China

    Xuan Lu

  3. School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China

    Furong Peng & Yuhua Qian

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  1. Furong Peng
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  2. Xuan Lu
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  5. Jianfeng Lu
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Correspondence to Jianfeng Lu.

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Peng, F., Lu, X., Ma, C. et al. Multi-level preference regression for cold-start recommendations. Int. J. Mach. Learn. & Cyber. 9, 1117–1130 (2018). https://doi.org/10.1007/s13042-017-0635-2

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