论文标题
HAM：连续推荐的混合协会模型
HAM: Hybrid Associations Models for Sequential Recommendation
论文作者
论文摘要
顺序建议旨在识别并推荐用户购买/评级轨迹的用户最有可能购买/审核的下几个项目。它成为帮助用户从各种选项中选择喜欢的项目的有效工具。在此手稿中，我们开发了混合关联模型（HAM），以使用三个因素生成顺序建议：1）用户的长期偏好，2）用户最近的购买/评分中的顺序，高阶和低阶关联模式，以及3）这些项目中的协同作用。 HAM使用简单的合并来表示关联中的一组项目，而元素依据产品来表示任意订单的项目协同作用。我们将HAM模型与三种不同的实验设置中的六个公共基准数据集中的最新最新方法进行了比较。我们的实验结果表明，在所有实验环境中，HAM模型在所有实验环境中的表现都显着胜过最高的现状，其改善高达46.6％。此外，我们在测试中的运行时性能比较表明，HAM模型比最先进的方法高得多，并且能够达到高达139.7倍的大速速度。
Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user's purchase/rating trajectories. It becomes an effective tool to help users select favorite items from a variety of options. In this manuscript, we developed hybrid associations models (HAM) to generate sequential recommendations using three factors: 1) users' long-term preferences, 2) sequential, high-order and low-order association patterns in the users' most recent purchases/ratings, and 3) synergies among those items. HAM uses simplistic pooling to represent a set of items in the associations, and element-wise product to represent item synergies of arbitrary orders. We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings. Our experimental results demonstrate that HAM models significantly outperform the state of the art in all the experimental settings, with an improvement as much as 46.6%. In addition, our run-time performance comparison in testing demonstrates that HAM models are much more efficient than the state-of-the-art methods, and are able to achieve significant speedup as much as 139.7 folds.
