25/07/2020

Automated embedding size search in deep recommender systems

Haochen Liu, Xiangyu Zhao, Chong Wang, Xiaobing Liu, Jiliang Tang

Keywords: embedding, recommender system, AutoML

Abstract Paper Similar Papers

Abstract: Deep recommender systems have achieved promising performance on real-world recommendation tasks. They typically represent users and items in a low-dimensional embedding space and then feed the embeddings into the following deep network structures for prediction. Traditional deep recommender models often adopt uniform and fixed embedding sizes for all the users and items. However, such design is not optimal in terms of not only the recommendation performance and but also the space complexity. In this paper, we propose to dynamically search the embedding sizes for different users and items and introduce a novel embedding size adjustment policy network (ESAPN). ESAPN serves as an automated reinforcement learning agent to adaptively search appropriate embedding sizes for users and items. Different from existing works, our model performs hard selection on different embedding sizes, which leads to a more accurate selection and decreases the storage space. We evaluate our model under the streaming setting on two real-world benchmark datasets. The results show that our proposed framework outperforms representative baselines. Moreover, our framework is demonstrated to be robust to the cold-start problem and reduce memory consumption by around 40

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