Sequential recommendation predicts users' next behaviors with their historical interactions. Recommending with longer sequences improves recommendation accuracy and increases the degree of personalization. As sequences get longer, existing works have not yet addressed the following two main challenges. Firstly, modeling long-range intra-sequence dependency is difficult with increasing sequence lengths. Secondly, it requires efficient memory and computational speeds. In this paper, we propose a Sparse Attentive Memory (SAM) network for long sequential user behavior modeling. SAM supports efficient training and real-time inference for user behavior sequences with lengths on the scale of thousands. In SAM, we model the target item as the query and the long sequence as the knowledge database, where the former continuously elicits relevant information from the latter. SAM simultaneously models target-sequence dependencies and long-range intra-sequence dependencies with O(L) complexity and O(1) number of sequential updates, which can only be achieved by the self-attention mechanism with O(L^2) complexity. Extensive empirical results demonstrate that our proposed solution is effective not only in long user behavior modeling but also on short sequences modeling. Implemented on sequences of length 1000, SAM is successfully deployed on one of the largest international E-commerce platforms. This inference time is within 30ms, with a substantial 7.30% click-through rate improvement for the online A/B test. To the best of our knowledge, it is the first end-to-end long user sequence modeling framework that models intra-sequence and target-sequence dependencies with the aforementioned degree of efficiency and successfully deployed on a large-scale real-time industrial recommender system.
翻译:序列建议预测用户的下一个行为及其历史互动。 以较长序列建议提高建议准确性并增加个性化程度。 随着序列的延长, 现有工程尚未解决以下两大挑战。 首先, 模拟长序列内依赖性是困难的, 随着序列长度的增加。 第二, 它需要高效的内存和计算速度。 在本文件中, 我们提议一个用于长顺序用户行为建模的“ 微缩缓冲内存( SAM) ” 网络。 SAM 支持对用户行为序列的高效培训和实时推断, 其长度为数千。 在 SAM 中, 我们将目标项目作为查询和长序列作为知识数据库的模型, 前者不断从后者获取相关信息。 SAM 同时, 目标序列依赖性和长序列内依赖性。 在O( L) 复杂性和 O(1) 序列更新数量上, 只能通过O( L) II 复杂自控机制实现。 广泛实验结果显示, 我们提议的系统内部时间序列中的目标序列项目作为查询和长序列, 不仅在长期用户行为模拟平台上有效, 并且 大规模的S- IM IM AS AS IM IM 上, AS AS AS AS AS AS AS IM AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AL AS AS AS AS AS AS AS AS