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.

翻译：以会议为基础的建议旨在根据特定会议为下一个用户兴趣项目提出建议。在本手稿中,我们开发了潜在的偏好增强混合关注模式(P2MAM),以便利用两个重要因素产生基于会议的建议:时间模式和用户预期偏好的估计。与现有方法不同,P2MAM用轻量度和有效的位置敏感关注机制来模拟时间模式。在P2MAM中,我们还利用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模型大大优于六个基准数据集中的最新方法,改进幅度高达19.2%。此外,我们的运行时性业绩比较表明,在测试期间,P2MAM模型比最佳基线方法效率高得多,平均速度高达47.7倍。