  Sequential recommender systems (SRSs) aim to suggest next item for a user
based on her historical interaction sequences. Recently, many research efforts
have been devoted to attenuate the influence of noisy items in sequences by
either assigning them with lower attention weights or discarding them directly.
The major limitation of these methods is that the former would still prone to
overfit noisy items while the latter may overlook informative items. To the
end, in this paper, we propose a novel model named Multi-level Sequence
Denoising with Cross-signal Contrastive Learning (MSDCCL) for sequential
recommendation. To be specific, we first introduce a target-aware user interest
extractor to simultaneously capture users' long and short term interest with
the guidance of target items. Then, we develop a multi-level sequence denoising
module to alleviate the impact of noisy items by employing both soft and hard
signal denoising strategies. Additionally, we extend existing curriculum
learning by simulating the learning pattern of human beings. It is worth noting
that our proposed model can be seamlessly integrated with a majority of
existing recommendation models and significantly boost their effectiveness.
Experimental studies on five public datasets are conducted and the results
demonstrate that the proposed MSDCCL is superior to the state-of-the-art
baselines. The source code is publicly available at
https://github.com/lalunex/MSDCCL/tree/main.
