Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Contrastive Self-supervised Sequential Recommendation with Robust
  Augmentation   Sequential Recommendationdescribes a set of techniques to model dynamic user
behavior in order to predict future interactions in sequential user data. At
their core, such approaches model transition probabilities between items in a
sequence, whether through Markov chains, recurrent networks, or more recently,
Transformers. However both old and new issues remain, including data-sparsity
and noisy data; such issues can impair the performance, especially in complex,
parameter-hungry models. In this paper, we investigate the application of
contrastive Self-Supervised Learning (SSL) to the sequential recommendation, as
a way to alleviate some of these issues. Contrastive SSL constructs
augmentations from unlabelled instances, where agreements among positive pairs
are maximized. It is challenging to devise a contrastive SSL framework for a
sequential recommendation, due to its discrete nature, correlations among
items, and skewness of length distributions. To this end, we propose a novel
framework, Contrastive Self-supervised Learning for sequential Recommendation
(CoSeRec). We introduce two informative augmentation operators leveraging item
correlations to create high-quality views for contrastive learning.
Experimental results on three real-world datasets demonstrate the effectiveness
of the proposed method on improving model performance and the robustness
against sparse and noisy data. Our implementation is available online at
\url{https://github.com/YChen1993/CoSeRec}

23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph