  Collaborative filtering (CF) is the key technique for recommender systems.
Pure CF approaches exploit the user-item interaction data (e.g., clicks, likes,
and views) only and suffer from the sparsity issue. Items are usually
associated with content information such as unstructured text (e.g., abstracts
of articles and reviews of products). CF can be extended to leverage text. In
this paper, we develop a unified neural framework to exploit interaction data
and content information seamlessly. The proposed framework, called LCMR, is
based on memory networks and consists of local and centralized memories for
exploiting content information and interaction data, respectively. By modeling
content information as local memories, LCMR attentively learns what to exploit
with the guidance of user-item interaction. On real-world datasets, LCMR shows
better performance by comparing with various baselines in terms of the hit
ratio and NDCG metrics. We further conduct analyses to understand how local and
centralized memories work for the proposed framework.
