论文标题
神经专心的多视频机器
Neural Attentive Multiview Machines
论文作者
论文摘要
多视图表示学习中的一个重要问题是找到有关手头特定任务的最佳视图组合。为此，我们介绍了NAM：一种神经关注的多视频机器，该机器通过采用新颖的注意机制来学习多视项目表示和相似性。 NAM利用多个信息来源，并自动量化其相关性方面的相关性。最后，NAM的非常实际的优势是它对数据集的情况的鲁棒性，而视图却缺少。我们演示了NAM对电影和应用建议的任务的有效性。我们的评估表明，NAM在项目建议任务（包括冷启动场景）上的替代多视图方法胜过单视图模型。
An important problem in multiview representation learning is finding the optimal combination of views with respect to the specific task at hand. To this end, we introduce NAM: a Neural Attentive Multiview machine that learns multiview item representations and similarity by employing a novel attention mechanism. NAM harnesses multiple information sources and automatically quantifies their relevancy with respect to a supervised task. Finally, a very practical advantage of NAM is its robustness to the case of dataset with missing views. We demonstrate the effectiveness of NAM for the task of movies and app recommendations. Our evaluations indicate that NAM outperforms single view models as well as alternative multiview methods on item recommendations tasks, including cold-start scenarios.
