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Cold-start recommendation issues refer to the recommendation task about new users and items, lots of work has been made to solve this problem. Model agnostic meta-learning (MAML) is a popular paradigm recently, which is used to train models that are able to learn and can be generalized. The key idea underlying MAML is to train the model’s initial parameters such that the model has maximal performance on a new task after the parameters have been updated through one or more gradient steps computed with a small amount of data from that new task. Inspired by the thoughts, we regard cold-start recommendation issues as few-shot meta-learning problem and propose meta-learning tower network (MLTN). Then we formalize the task for each user and train the model’s parameters in meta-learning optimization way. Extensive experiments on both industrial datasets and public datasets demonstrate the superiority of MLTN. |
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Keywords:Deep Learning, Neural Network, Meta Learning, Cold-Start Recommendation |
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