Collective embedding for neural context-aware recommender systems

F. Costa, Peter Dolog
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引用次数: 17

Abstract

Context-aware recommender systems consider contextual features as additional information to predict user's preferences. For example, the recommendations could be based on time, location, or the company of other people. Among the contextual information, time became an important feature because user preferences tend to change over time or be similar in the near future. Researchers have proposed different models to incorporate time into their recommender system, however, the current models are not able to capture specific temporal patterns. To address the limitation observed in previous works, we propose Collective embedding for Neural Context-Aware Recommender Systems (CoNCARS). The proposed solution jointly model the item, user and time embeddings to capture temporal patterns. Then, CoNCARS use the outer product to model the user-item-time correlations between dimensions of the embedding space. The hidden features feed our Convolutional Neural Networks (CNNs) to learn the non-linearities between the different features. Finally, we combine the output from our CNNs in the fusion layer and then predict the user's preference score. We conduct extensive experiments on real-world datasets, demonstrating CoNCARS improves the top-N item recommendation task and outperform the state-of-the-art recommendation methods.
神经上下文感知推荐系统的集体嵌入
上下文感知推荐系统将上下文特征作为预测用户偏好的附加信息。例如,推荐可以基于时间、地点或其他人的陪伴。在上下文信息中,时间成为一个重要的特征,因为用户的偏好往往会随着时间的推移而变化,或者在不久的将来会变得相似。研究人员提出了不同的模型来将时间整合到他们的推荐系统中,然而,目前的模型不能捕获特定的时间模式。为了解决在以前的工作中观察到的局限性,我们提出了神经上下文感知推荐系统(CoNCARS)的集体嵌入。提出的解决方案联合对项目、用户和时间嵌入进行建模,以捕获时间模式。然后,CoNCARS使用外部积来建模嵌入空间维度之间的用户-项目-时间相关性。这些隐藏的特征为我们的卷积神经网络(cnn)提供了学习不同特征之间的非线性的信息。最后,我们在融合层中结合cnn的输出,然后预测用户的偏好得分。我们在真实世界的数据集上进行了大量的实验,证明CoNCARS改进了top-N项推荐任务,并且优于最先进的推荐方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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