Industrial recommender systems usually employ multi-source data to improve the recommendation quality, while effectively sharing information between different data sources remain a challenge. In this paper, we introduce a novel Multi-View Approach with Hybrid Attentive Networks (MV-HAN) for contents retrieval at the matching stage of recommender systems. The proposed model enables high-order feature interaction from various input features while effectively transferring knowledge between different types. By employing a well-placed parameters sharing strategy, the MV-HAN substantially improves the retrieval performance in sparse types. The designed MV-HAN inherits the efficiency advantages in the online service from the two-tower model, by mapping users and contents of different types into the same features space. This enables fast retrieval of similar contents with an approximate nearest neighbor algorithm. We conduct offline experiments on several industrial datasets, demonstrating that the proposed MV-HAN significantly outperforms baselines on the content retrieval tasks. Importantly, the MV-HAN is deployed in a real-world matching system. Online A/B test results show that the proposed method can significantly improve the quality of recommendations.
翻译:工业推荐人系统通常采用多源数据来提高建议质量,而不同数据来源之间有效分享信息仍然是一项挑战。在本文件中,我们采用了与混合强化网络(MV-HAN)的新的多查看方法,以便在推荐人系统的匹配阶段检索内容。拟议模型使各种输入特征的高端特征互动,同时在不同类型之间有效转让知识。采用位置良好的参数共享战略,MV-HAN大大改进了稀有类型的检索性能。设计的MV-HAN从二到二模式中继承了在线服务的效率优势,将不同类型用户和内容映射到相同的功能空间。这样可以以近邻的算法快速检索类似内容。我们在几个工业数据集上进行离线实验,表明拟议的MV-HAN大大超过内容检索任务的基准。重要的是,MV-HAN被部署在一个真实世界匹配系统中。在线A/B测试结果显示,拟议的方法可以大大改进建议的质量。