An anonymous user-behavior session in an e-commerce platform is a time-stamped series of sequential implicit feedback (e.g., clicks and orders of items) in a short period, without user profiles available (e.g., the non-logged-in users). The accurate modeling of such sessions is crucial for distributed representation learning in user profiling and item embedding. It broadly spans the recommendation scenarios with capabilities, such as next-click item prediction. The statistics of sessions provided by one of the largest e-commerce platforms indicate that a user generally has multiple interests in a session simultaneously, and the interests may be interleaved. Recent advances in recurrent neural networks and attention mechanisms have led to promising approaches for modeling such sessions. However, few of the existing models explicitly consider the effects of multiple interleaving interests. Through specific data analysis, we find there are two characteristics present in those sessions: 1) contiguous items usually share the same user interest(e.g., category), which can measure the intensity of interests in the current window scope (i.e., local features of interests); 2) each interest repeatedly occurs in a session, which shows its importance in the current session (i.e., global features of interests). Based on the observations, we present a novel framework that provides M ultiple I nterleaving I nterests M odeling with the following contributions: 1) a local layer is adopted to extract the local features of interests by the convolution operations; 2) a global layer is utilized to capture the global features of interests in the current sequence by considering the frequency of items; 3) an Interest-GRU layer is adopted to track each interest’s sequential evolution by fusing local and global features. Experimental results of the next-click prediction task on two real-world datasets demonstrate that our proposed method significantly outperforms state-of-the-art models.

电子商务平台中的匿名用户行为会话是在短时间内带有时间戳的一系列连续隐式反馈（例如，点击和项目顺序），而没有可用的用户配置文件（例如，非登录用户）。此类会话的准确建模对于用户配置文件和项目嵌入中的分布式表示学习至关重要。它广泛地涵盖具有推荐功能的建议方案，例如，单击项预测。由最大的电子商务平台之一提供的会话的统计信息表明，用户通常在一个会话中同时具有多个兴趣，并且兴趣可以交错。递归神经网络和注意力机制的最新进展已导致为此类会议建模的有前途的方法。但是，很少有现有模型明确考虑多重交织兴趣的影响。通过特定的数据分析，我们发现这些会话具有两个特征：1）连续的项目通常具有相同的用户兴趣（例如类别），它可以衡量当前窗口范围内的兴趣强度（即兴趣的局部特征）；2）每个兴趣重复出现在一个会话中，这表明了它在当前会话中的重要性（即兴趣的全局特征）。基于该观察，我们提出了一个新颖的框架，提供中号ultiple我nterleaving我nterests中号具有以下贡献：1）采用局部层，通过卷积运算提取感兴趣的局部特征；2）通过考虑项目的频率，利用全局层来捕获当前序列中感兴趣的全局特征；3）采用兴趣层-GRU层，通过融合局部和全局特征来跟踪每个兴趣层的顺序演变。在两个真实的数据集上进行的下次点击预测任务的实验结果表明，我们提出的方法明显优于最新模型。