Symmetric Metric Learning with Adaptive Margin for Recommendation


  • Mingming Li Chinese Academy of Sciences
  • Shuai Zhang The University of New South Wales
  • Fuqing Zhu Chinese Academy of Sciences
  • Wanhui Qian Chinese Academy of Sciences
  • Liangjun Zang Chinese Academy of Sciences
  • Jizhong Han Chinese Academy of Sciences
  • Songlin Hu Chinese Academy of Sciences



Metric learning based methods have attracted extensive interests in recommender systems. Current methods take the user-centric way in metric space to ensure the distance between user and negative item to be larger than that between the current user and positive item by a fixed margin. While they ignore the relations among positive item and negative item. As a result, these two items might be positioned closely, leading to incorrect results. Meanwhile, different users usually have different preferences, the fixed margin used in those methods can not be adaptive to various user biases, and thus decreases the performance as well. To address these two problems, a novel Symmetic Metric Learning with adaptive margin (SML) is proposed. In addition to the current user-centric metric, it symmetically introduces a positive item-centric metric which maintains closer distance from positive items to user, and push the negative items away from the positive items at the same time. Moreover, the dynamically adaptive margins are well trained to mitigate the impact of bias. Experimental results on three public recommendation datasets demonstrate that SML produces a competitive performance compared with several state-of-the-art methods.




How to Cite

Li, M., Zhang, S., Zhu, F., Qian, W., Zang, L., Han, J., & Hu, S. (2020). Symmetric Metric Learning with Adaptive Margin for Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 4634-4641.



AAAI Technical Track: Machine Learning