计算机科学
背景(考古学)
深度学习
协同过滤
人工智能
特征(语言学)
机器学习
推荐系统
上下文模型
数据科学
情报检索
语言学
生物
哲学
古生物学
对象(语法)
作者
Moshe Unger,Alexander Tuzhilin,Amit Livne
出处
期刊:ACM transactions on management information systems
[Association for Computing Machinery]
日期:2020-05-22
卷期号:11 (2): 1-15
被引量:64
摘要
In this article, we suggest a novel deep learning recommendation framework that incorporates contextual information into neural collaborative filtering recommendation approaches. Since context is often represented by dynamic and high-dimensional feature space in multiple applications and services, we suggest to model contextual information in various ways for multiple purposes, such as rating prediction, generating top-k recommendations, and classification of users’ feedback. Specifically, based on the suggested framework, we propose three deep context-aware recommendation models based on explicit, unstructured, and structured latent representations of contextual data derived from various contextual dimensions (e.g., time, location, user activity). Offline evaluation on three context-aware datasets confirms that our proposed deep context-aware models surpass state-of-the-art context-aware methods. We also show that utilizing structured latent contexts in the proposed deep recommendation framework achieves significantly better performance than the other context-aware models on all datasets.
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