  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.
