Recommender systems have played a vital role in online platforms due to the ability of incorporating users' personal tastes. Beyond accuracy, diversity has been recognized as a key factor in recommendation to broaden user's horizons as well as to promote enterprises' sales. However, the trading-off between accuracy and diversity remains to be a big challenge, and the data and user biases have not been explored yet. In this paper, we develop an adaptive learning framework for accurate and diversified recommendation. We generalize recent proposed bi-lateral branch network in the computer vision community from image classification to item recommendation. Specifically, we encode domain level diversity by adaptively balancing accurate recommendation in the conventional branch and diversified recommendation in the adaptive branch of a bilateral branch network. We also capture user level diversity using a two-way adaptive metric learning backbone network in each branch. We conduct extensive experiments on three real-world datasets. Results demonstrate that our proposed approach consistently outperforms the state-of-the-art baselines.

中文翻译：

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使用双边分支网络推荐准确多样的商品

推荐系统由于能够融合用户的个人喜好，因此在在线平台中起着至关重要的作用。除准确性外，多样性已被认为是推荐以扩大用户视野并促进企业销售的关键因素。但是，准确性和多样性之间的权衡仍然是一个很大的挑战，并且尚未探索数据和用户偏见。在本文中，我们为准确和多样化的推荐开发了一个自适应学习框架。我们将计算机视觉社区中最近提出的双边分支网络概括化，从图像分类到项目推荐。特别，我们通过在传统分支中的准确推荐与双边分支网络的自适应分支中的多样化推荐之间进行自适应平衡来对域级多样性进行编码。我们还使用每个分支中的双向自适应度量学习骨干网来捕获用户级别的多样性。我们对三个现实世界的数据集进行了广泛的实验。结果表明，我们提出的方法始终优于最新的基准。