Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Comprehensive Empirical Evaluation of Deep Learning Approaches for
  Session-based Recommendation in E-Commerce   Boosting sales of e-commerce services is guaranteed once users find more
matching items to their interests in a short time. Consequently, recommendation
systems have become a crucial part of any successful e-commerce services.
Although various recommendation techniques could be used in e-commerce, a
considerable amount of attention has been drawn to session-based recommendation
systems during the recent few years. This growing interest is due to the
security concerns in collecting personalized user behavior data, especially
after the recent general data protection regulations. In this work, we present
a comprehensive evaluation of the state-of-the-art deep learning approaches
used in the session-based recommendation. In session-based recommendation, a
recommendation system counts on the sequence of events made by a user within
the same session to predict and endorse other items that are more likely to
correlate with his/her preferences. Our extensive experiments investigate
baseline techniques (\textit{e.g.,} nearest neighbors and pattern mining
algorithms) and deep learning approaches (\textit{e.g.,} recurrent neural
networks, graph neural networks, and attention-based networks). Our evaluations
show that advanced neural-based models and session-based nearest neighbor
algorithms outperform the baseline techniques in most of the scenarios.
However, we found that these models suffer more in case of long sessions when
there exists drift in user interests, and when there is no enough data to model
different items correctly during training. Our study suggests that using hybrid
models of different approaches combined with baseline algorithms could lead to
substantial results in session-based recommendations based on dataset
characteristics. We also discuss the drawbacks of current session-based
recommendation algorithms and further open research directions in this field.

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The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
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The categories of this paper are: chem-ph
16
The categories of this paper are: stat
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
2
The categories of this paper are: math
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The categories of this paper are: cmp-lg