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:序列建议旨在从历史互动中捕捉用户的偏好, 从而预测他们要互动的下一个项目。 序列建议方法通常假定用户历史互动中的所有项目都反映其偏好和项目之间的过渡模式。 然而, 真实世界互动数据不完美, 原因是 (一) 用户可能会错误点击项目, 即所谓的误击不相关的项目, 以及 (二) 用户可能会错过项目, 即由于不准确的建议而未披露相关项目 。 要解决上面列出的两个问题, 我们提议 STEAM, 一个自译自算进行广泛的 EquentiAl recoMender 。 STEAM 首先通过调整错误的点击和/ 或缺失的项目来纠正输入项目顺序 。 我们设计一个项目错误的正确性更正器, 能够根据不准确性选择每个序列的操作类型 。 操作类型是“ 继续, 、 删除 和 插入“ 插入 ” 。 ( 校正 ) 校正项目, 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校正、 校。</s>