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历史交互中捕捉其偏好，以预测用户接下来可能交互的下一项。序列推荐方法通常假设用户历史交互中的所有项目均反映其偏好及项目之间的转移模式。然而，现实世界的交互数据存在不完美性：（i）用户可能错误点击项目，即对不相关项目的误点击；（ii）用户可能遗漏项目，即因推荐不准确而未能曝光的相关项目。为解决上述两个问题，我们提出一种自纠错序列推荐器STEAM。STEAM首先通过调整误点击和/或遗漏项目来纠正输入项目序列，然后利用纠正后的项目序列训练推荐器并预测下一项。我们设计了一个逐项纠错器，能够为序列中的每个项目自适应选择操作类型，操作包括"保留"、"删除"和"插入"。为训练该逐项纠错器且无需额外标注，我们设计两种自监督学习机制：（i）删除纠错（即删除随机插入的项目）；（ii）插入纠错（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错器与推荐器集成。在三个真实数据集上的广泛实验表明，STEAM优于最先进的序列推荐基线方法。深入分析证实，STEAM受益于学习纠正原始项目序列。