An Efficient Adaptive Transfer Neural Network for Social-aware Recommendation
An Efficient Adaptive Transfer Neural Network for Social-aware Recommendation
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DOI：
10.1145/3331184.3331192
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发表时间：
2019-07
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影响因子：
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通讯作者：
C. Chen;Min Zhang;Chenyang Wang;Weizhi Ma;Minming Li;Yiqun Liu;Shaoping Ma
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文献类型：
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作者：
C. Chen;Min Zhang;Chenyang Wang;Weizhi Ma;Minming Li;Yiqun Liu;Shaoping Ma
以往的许多研究都试图利用其他领域的信息来实现更好的推荐性能。近年来，社交信息通过迁移学习框架有效地改善了推荐结果，迁移部分从项目域和社交域两个方面帮助学习用户的偏好。然而，现有的方法中有两个关键问题没有得到很好的考虑：1)通常采用静态转移机制来共享用户在物品和社交领域之间的共同偏好，这在不同用户的共享度和信息丰富度不同的现实生活中并不健壮。因此，非个性化的转移方案可能是不充分和不成功的。2)以往的神经网络推荐方法大多依赖负采样来提高计算效率，这使得它们对采样策略高度敏感，在实际应用中很难获得最优结果。针对上述问题，我们提出了一种高效的自适应传递神经网络。通过引入注意机制，该模型自动为每个用户分配一个个性化的迁移方案。此外，我们设计了一种有效的优化方法来从整个训练集学习，而不需要负采样，并进一步将其扩展到支持多任务学习。在三个真实的公共数据集上的大量实验表明，我们的EATNN方法在Top-K推荐任务上的性能一直优于最先进的方法，特别是对于项目交互很少的冷启动用户。值得注意的是，EATNN在训练效率方面表现出了显著的优势，这使得它更适合应用于实际的电子商务场景。代码可在(https://github.com/chenchongthu/EATNN).)上获得
Many previous studies attempt to utilize information from other domains to achieve better performance of recommendation. Recently, social information has been shown effective in improving recommendation results with transfer learning frameworks, and the transfer part helps to learn users' preferences from both item domain and social domain. However, two vital issues have not been well-considered in existing methods: 1) Usually, a static transfer scheme is adopted to share a user's common preference between item and social domains, which is not robust in real life where the degrees of sharing and information richness are varied for different users. Hence a non-personalized transfer scheme may be insufficient and unsuccessful. 2) Most previous neural recommendation methods rely on negative sampling in training to increase computational efficiency, which makes them highly sensitive to sampling strategies and hence difficult to achieve optimal results in practical applications. To address the above problems, we propose an Efficient Adaptive Transfer Neural Network (EATNN). By introducing attention mechanisms, the proposed model automatically assign a personalized transfer scheme for each user. Moreover, we devise an efficient optimization method to learn from the whole training set without negative sampling, and further extend it to support multi-task learning. Extensive experiments on three real-world public datasets indicate that our EATNN method consistently outperforms the state-of-the-art methods on Top-K recommendation task, especially for cold-start users who have few item interactions. Remarkably, EATNN shows significant advantages in training efficiency, which makes it more practical to be applied in real E-commerce scenarios. The code is available at (https://github.com/chenchongthu/EATNN).