Hyper-parameter Optimization of Session-based Recommendation Systems
Date
2021
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Abstract
This thesis proposes a comprehensive framework to recommend session-based
algorithms and tune their hyperparameters. In the first part of this study, we present a
comprehensive evaluation of the state-of-the-art deep learning approaches used in the
session-based recommendation. Furthermore, we present an evaluation of neural-based
models’ performance using the AutoML Neural Network Intelligence framework. In
session-based recommendation, the system counts on the sequence of events made by a
user within the same session to predict and endorse other more likely items to correlate
with his preferences. Our extensive experiments investigate baseline techniques (e.g.,
nearest neighbors and pattern mining algorithms) and deep learning approaches (e.g.,
recurrent neural networks, graph neural networks, and attention-based networks). The
first evaluation shows that advanced neural network models outperform the baseline
techniques in most of the scenarios. However, we found that these models suffer more
in long sessions when there is a drift in user interests, and there is not enough data
to correctly model different items during training. The first study suggests that using
hybrid models of different approaches combined with baseline algorithms could result in
session-based recommendations based on dataset characteristics. The second evaluation
shows that AutoML improved the performance of neural-based models by using a stateof-
the-art algorithm such as Bayesian Optimization and hyperband (BOHB) to replace
the random selection of hyper-parameters.
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Keywords
Machine Learning, Deep Learning, Automated Machine Learning, hyperparameter optimization, Recommendation systems