论文标题
预期优先增强了基于会话建议的混合专注模型
Prospective Preference Enhanced Mixed Attentive Model for Session-based Recommendation
论文作者
论文摘要
基于会话的建议旨在根据给定的会话为下一项用户的兴趣生成建议。在本手稿中，我们开发了预期的优先增强的混合专注模型（P2MAM），以使用两个重要因素生成基于会话的建议：时间模式和用户预期偏好的估计。与现有方法不同，P2MAM使用轻量重量对时间模式进行建模，同时有效地对位置敏感的注意机制。在P2MAM中，我们还利用了用户预期偏好的估计来表示重要项目，并产生更好的建议。我们的实验结果表明，P2MAM模型在六个基准数据集中的最先进方法大大优于其最先进的方法，其改善高达19.2％。此外，我们的运行时间性能比较表明，在测试过程中，P2MAM模型比最佳基线方法高得多，其平均速度显着为47.7倍。
Session-based recommendation aims to generate recommendations for the next item of users' interest based on a given session. In this manuscript, we develop prospective preference enhanced mixed attentive model (P2MAM) to generate session-based recommendations using two important factors: temporal patterns and estimates of users' prospective preferences. Unlike existing methods, P2MAM models the temporal patterns using a light-weight while effective position-sensitive attention mechanism. In P2MAM, we also leverage the estimate of users' prospective preferences to signify important items, and generate better recommendations. Our experimental results demonstrate that P2MAM models significantly outperform the state-of-the-art methods in six benchmark datasets, with an improvement as much as 19.2%. In addition, our run-time performance comparison demonstrates that during testing, P2MAM models are much more efficient than the best baseline method, with a significant average speedup of 47.7 folds.
