论文标题
使用深度学习和嵌入的上下文感知推荐系统的系统评价
A Systematic Review on Context-Aware Recommender Systems using Deep Learning and Embeddings
论文作者
论文摘要
推荐系统是改善用户如何在Web系统中找到相关信息的工具，因此他们不会面对太多信息。为了产生更好的建议，应在建议过程中使用信息上下文。创建了上下文感知的推荐系统，完成最新结果并改善传统推荐系统。有许多构建推荐系统的方法，而领域最突出的两个进步是使用嵌入来表示推荐系统中的数据，并使用深度学习体系结构来向用户生成建议。系统评价采用了一种正式和系统的方法来进行书目审查，并通过分析发布的相关研究来识别和评估某些研究领域的所有研究。进行了系统的审查，以了解如何应用深度学习和嵌入技术来改善上下文感知的推荐系统。我们总结了用于创建使用它们的域和域的体系结构。
Recommender Systems are tools that improve how users find relevant information in web systems, so they do not face too much information. In order to generate better recommendations, the context of information should be used in the recommendation process. Context-Aware Recommender Systems were created, accomplishing state-of-the-art results and improving traditional recommender systems. There are many approaches to build recommender systems, and two of the most prominent advances in area have been the use of Embeddings to represent the data in the recommender system, and the use of Deep Learning architectures to generate the recommendations to the user. A systematic review adopts a formal and systematic method to perform a bibliographic review, and it is used to identify and evaluate all the research in certain area of study, by analyzing the relevant research published. A systematic review was conducted to understand how the Deep Learning and Embeddings techniques are being applied to improve Context-Aware Recommender Systems. We summarized the architectures that are used to create those and the domains that they are used.
