Adversarial tensor factorization for context-aware recommendation
Adversarial tensor factorization for context-aware recommendation
复制标题
DOI：
10.1145/3298689.3346987
复制
发表时间：
2019-09
期刊：
影响因子：
--
通讯作者：
Huiyuan Chen;Jing Li
中科院分区：
文献类型：
--
作者：
Huiyuan Chen;Jing Li
时间、位置或标签等上下文因素可能会影响用户对特定项目的偏好。因此，与仅基于用户-项目交互的传统推荐相比，上下文感知推荐对于提高推荐系统的质量和可解释性至关重要。张量分解机由于能够以一种统一的方式集成用户、项目和上下文因素，因此实现了最先进的性能。然而，很少有工作关注上下文感知推荐系统的稳健性。由于观察到的张量的稀疏性和张量分解的多线性性质，提高基于张量的模型的鲁棒性具有挑战性。在本文中，我们提出了 ATF，一种结合张量分解和对抗性学习的模型，用于上下文感知推荐。这样做使我们能够获得张量分解的好处，同时增强推荐模型的稳健性，从而提高其最终性能。对两个现实世界数据集的实证研究表明，所提出的方法优于标准的基于张量的方法。
Contextual factors such as time, location, or tag, can affect user preferences for a particular item. Context-aware recommendations are thus critical to improve both quality and explainability of recommender systems, compared to traditional recommendations that are solely based on user-item interactions. Tensor factorization machines have achieved the state-of-the-art performance due to their capability of integrating users, items, and contextual factors in one unify way. However, few work has focused on the robustness of a context-aware recommender system. Improving the robustness of a tensor-based model is challenging due to the sparsity of the observed tensor and the multi-linear nature of tensor factorization. In this paper, we propose ATF, a model that combines tensor factorization and adversarial learning for context-aware recommendations. Doing so allows us to reap the benefits of tensor factorization, while enhancing the robustness of a recommender model, and thus improves its eventual performance. Empirical studies on two real-world datasets show that the proposed method outperforms standard tensor-based methods.