询问GRU：深度文本推荐的多任务学习
Ask the GRU: Multi-Task Learning for Deep Text Recommendations
摘要：
在各种应用领域中，要推荐给用户的内容与文本相关联。这包括研究论文，电影与相关的情节摘要，新闻文章，博客文章，等等。基于潜在因素模型的推荐方法可以自然地扩展到利用文本，通过采用从文本到因素的显式映射。这使得能够推荐新的、看不见的内容，并且可以更好地概括，因为所有项目的因素都是由紧凑参数化模型产生的。以前的工作已经使用主题模型或词嵌入的平均值来进行这种映射。在本文中，我们提出了一种利用深度递归神经网络将文本序列编码为潜在向量的方法，特别是在协同过滤任务上进行端到端训练的门控递归单元（GRU）。对于科学论文推荐的任务，这产生了具有显著更高准确性的模型。在冷启动场景中，我们击败了之前的最先进的技术，所有这些技术都忽略了词序。通过多任务学习进一步提高性能，其中文本编码器网络被训练用于内容推荐和项目元数据预测的组合。这正则化的协同过滤模型，改善了稀疏性的问题，观察到的评级矩阵。
英文摘要：
In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping. In this paper we present a method leveraging deep recurrent neural networks to encode the text sequence into a latent vector, specifically gated recurrent units (GRUs) trained end-to-end on the collaborative filtering task. For the task of scientific paper recommendation, this yields models with significantly higher accuracy. In cold-start scenarios, we beat the previous state-of-the-art, all of which ignore word order. Performance is further improved by multi-task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction. This regularizes the collaborative filtering model, ameliorating the problem of sparsity of the observed rating matrix.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
