Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user's historical interactions reflect her/his preferences and transition patterns between items. However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations. To tackle the two issues listed above, we propose STEAM, a Self-correcTing sEquentiAl recoMmender. STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items. It then uses the corrected item sequence to train a recommender and make the next item prediction.We design an item-wise corrector that can adaptively select one type of operation for each item in the sequence. The operation types are 'keep', 'delete' and 'insert.' In order to train the item-wise corrector without requiring additional labeling, we design two self-supervised learning mechanisms: (i) deletion correction (i.e., deleting randomly inserted items), and (ii) insertion correction (i.e., predicting randomly deleted items). We integrate the corrector with the recommender by sharing the encoder and by training them jointly. We conduct extensive experiments on three real-world datasets and the experimental results demonstrate that STEAM outperforms state-of-the-art sequential recommendation baselines. Our in-depth analyses confirm that STEAM benefits from learning to correct the raw item sequences.

翻译：序列推荐旨在从用户的历史交互中捕捉其偏好，从而预测他们下一个会交互的项目。序列推荐方法通常假设用户历史交互中的所有项目都反映了其偏好以及项目之间的转换模式。然而，现实世界的交互数据并不完美，原因在于：(i) 用户可能误点击项目，即所谓的对无关项目的误触；(ii) 用户可能错过项目，即由于不准确的推荐而未曝光的相关项目。为了解决上述两个问题，我们提出了STEAM，一个自纠正的序列推荐器。STEAM首先通过调整误触和/或错过的项目来纠正输入的项目序列，然后使用纠正后的项目序列训练推荐器并进行下一个项目预测。我们设计了一个逐项目纠正器，能够自适应地为序列中的每个项目选择一种操作类型，操作类型包括“保留”、“删除”和“插入”。为了在无需额外标注的情况下训练该逐项目纠正器，我们设计了两种自监督学习机制：(i) 删除纠正（即删除随机插入的项目），以及(ii) 插入纠正（即预测随机删除的项目）。我们通过共享编码器并联合训练的方式将纠正器与推荐器集成。我们在三个真实世界数据集上进行了大量实验，实验结果表明STEAM优于最先进的序列推荐基线。我们的深入分析证实，STEAM得益于学习纠正原始项目序列的能力。