Collaborative filtering (CF) is the key technique for recommender systems
(RSs). CF exploits user-item behavior interactions (e.g., clicks) only and
hence suffers from the data sparsity issue. One research thread is to integrate
auxiliary information such as product reviews and news titles, leading to
hybrid filtering methods. Another thread is to transfer knowledge from other
source domains such as improving the movie recommendation with the knowledge
from the book domain, leading to transfer learning methods. In real-world life,
no single service can satisfy a user's all information needs. Thus it motivates
us to exploit both auxiliary and source information for RSs in this paper. We
propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH)
methods for cross-domain recommendation with unstructured text in an end-to-end
manner. TMH attentively extracts useful content from unstructured text via a
memory module and selectively transfers knowledge from a source domain via a
transfer network. On two real-world datasets, TMH shows better performance in
terms of three ranking metrics by comparing with various baselines. We conduct
thorough analyses to understand how the text content and transferred knowledge
help the proposed model.

该论文介绍了一种使用神经模型实现跨领域推荐的方法，该方法结合协同过滤、混合过滤和迁移学习等技术。该方法使用记忆模块和转移网络从非结构化文本中提取有用内容并选择性地转移知识，实现了端到端的处理，并在实际数据集上取得了更好的推荐效果。