  One of the key challenges in Sequential Recommendation (SR) is how to extract
and represent user preferences. Traditional SR methods rely on the next item as
the supervision signal to guide preference extraction and representation. We
propose a novel learning strategy, named preference editing. The idea is to
force the SR model to discriminate the common and unique preferences in
different sequences of interactions between users and the recommender system.
By doing so, the SR model is able to learn how to identify common and unique
user preferences, and thereby do better user preference extraction and
representation. We propose a transformer based SR model, named MrTransformer
(Multi-preference Transformer), that concatenates some special tokens in front
of the sequence to represent multiple user preferences and makes sure they
capture different aspects through a preference coverage mechanism. Then, we
devise a preference editing-based self-supervised learning mechanism for
training MrTransformer which contains two main operations: preference
separation and preference recombination. The former separates the common and
unique user preferences for a given pair of sequences. The latter swaps the
common preferences to obtain recombined user preferences for each sequence.
Based on the preference separation and preference recombination operations, we
define two types of SSL loss that require that the recombined preferences are
similar to the original ones, and the common preferences are close to each
other.
  We carry out extensive experiments on two benchmark datasets. MrTransformer
with preference editing significantly outperforms state-of-the-art SR methods
in terms of Recall, MRR and NDCG. We find that long sequences whose user
preferences are harder to extract and represent benefit most from preference
editing.
