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顺序推荐模型的效果，据称有助于捕捉物品之间的转移模式。以往大多数利用侧信息的顺序推荐工作将物品ID与侧信息分开建模，这可能导致无法充分建模物品与其侧信息之间的关系。此外，在实际系统中，并非所有物品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物品特征字段的上下文，并用通用或特殊值（例如"未知"）进行填充，这可能导致次优性能。为解决带有侧信息的顺序推荐模型的局限性，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，提出统一任务——缺失信息填补（MII），该任务随机掩码给定物品序列中的某些特征字段（包括物品ID），然后迫使预测模型恢复它们。通过将下一物品视为缺失特征字段，顺序推荐可被建模为MII的一个特例。我们提出一种基于MII思想的顺序推荐模型——缺失信息填补推荐器（MIIR），该模型同时填补缺失的物品特征值并预测下一物品。我们为MIIR设计了密集融合自注意力（DFSA）机制，以捕捉物品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验表明，通过MII监督的MIIR在顺序推荐性能上显著优于最先进的基线模型。