Side information is being used extensively to improve the effectiveness of sequential recommendation models. It is said to help capture the transition patterns among items. Most previous work on sequential recommendation that uses side information models item IDs and side information separately, which may fail to fully model the relation between the items and their side information. Moreover, in real-world systems, not all values of item feature fields are available. This hurts the performance of models that rely on side information. Existing methods tend to neglect the context of missing item feature fields, and fill them with generic or special values, e.g., unknown, which might lead to sub-optimal performance. To address the limitation of sequential recommenders with side information, we define a way to fuse side information and alleviate the problem of missing side information by proposing a unified task, namely the missing information imputation (MII), which randomly masks some feature fields in a given sequence of items, including item IDs, and then forces a predictive model to recover them. By considering the next item as a missing feature field, sequential recommendation can be formulated as a special case of MII. We propose a sequential recommendation model, called missing information imputation recommender (MIIR), that builds on the idea of MII and simultaneously imputes missing item feature values and predicts the next item. We devise a dense fusion self-attention (DFSA) mechanism for MIIR to capture all pairwise relations between items and their side information. Empirical studies on three benchmark datasets demonstrate that MIIR, supervised by MII, achieves a significantly better sequential recommendation performance than state-of-the-art baselines.

翻译：侧信息被广泛用于提升序列推荐模型的有效性，据称有助于捕捉项目之间的转换模式。以往大多数利用侧信息的序列推荐工作将项目ID和侧信息分开建模，这可能无法充分建模项目与其侧信息之间的关系。此外，在现实系统中，并非所有项目特征字段的值都可用，这损害了依赖侧信息的模型的性能。现有方法往往忽略缺失项目特征字段的上下文，并使用通用或特殊值（例如“未知”）进行填充，这可能导致次优性能。为了解决带侧信息的序列推荐模型的局限性，我们定义了一种融合侧信息并缓解侧信息缺失问题的方法，提出一个统一任务，即缺失信息填补（MII），该任务随机屏蔽给定项目序列中的某些特征字段（包括项目ID），然后强制预测模型恢复它们。通过将下一个项目视为缺失特征字段，序列推荐可被表述为MII的一个特例。我们提出一个基于MII思想构建的序列推荐模型，称为缺失信息填补推荐器（MIIR），它同时填补缺失的项目特征值并预测下一个项目。我们为MIIR设计了一种密集融合自注意力（DFSA）机制，以捕获项目与其侧信息之间的所有成对关系。在三个基准数据集上的实证研究表明，由MII监督的MIIR在序列推荐性能上显著优于最先进的基线方法。