  Traditional sequential recommendation methods assume that users' sequence
data is clean enough to learn accurate sequence representations to reflect user
preferences. In practice, users' sequences inevitably contain noise (e.g.,
accidental interactions), leading to incorrect reflections of user preferences.
Consequently, some pioneer studies have explored modeling sequentiality and
correlations in sequences to implicitly or explicitly reduce noise's influence.
However, relying on only available intra-sequence information (i.e.,
sequentiality and correlations in a sequence) is insufficient and may result in
over-denoising and under-denoising problems (OUPs), especially for short
sequences. To improve reliability, we propose to augment sequences by inserting
items before denoising. However, due to the data sparsity issue and
computational costs, it is challenging to select proper items from the entire
item universe to insert into proper positions in a target sequence. Motivated
by the above observation, we propose a novel framework--Self-augmented Sequence
Denoising for sequential Recommendation (SSDRec) with a three-stage learning
paradigm to solve the above challenges. In the first stage, we empower SSDRec
by a global relation encoder to learn multi-faceted inter-sequence relations in
a data-driven manner. These relations serve as prior knowledge to guide
subsequent stages. In the second stage, we devise a self-augmentation module to
augment sequences to alleviate OUPs. Finally, we employ a hierarchical
denoising module in the third stage to reduce the risk of false augmentations
and pinpoint all noise in raw sequences. Extensive experiments on five
real-world datasets demonstrate the superiority of \model over state-of-the-art
denoising methods and its flexible applications to mainstream sequential
recommendation models. The source code is available at
https://github.com/zc-97/SSDRec.
