LCMR：用于非结构化文本协同过滤的局部和集中存储器
LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text
摘要：
协同过滤（CF）是推荐系统的关键技术。纯CF方法利用用户-项目交互数据（例如，点击、喜欢和观看），并且遭受稀疏性问题。项通常与诸如非结构化文本（例如，文章摘要和产品评论）。CF可以扩展为利用文本。在本文中，我们开发了一个统一的神经框架来无缝地利用交互数据和内容信息。所提出的框架，称为LCMR，是基于内存网络和本地和集中式存储器，分别利用内容信息和交互数据。通过将内容信息建模为本地记忆，LCMR在用户-项目交互的指导下认真学习要利用什么。在现实世界的数据集上，LCMR通过与各种基线的命中率和NDCG指标进行比较，显示出更好的性能。我们进一步进行分析，以了解本地和集中式记忆如何为拟议的框架工作。
英文摘要：
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.
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。
