PAPER 1: A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods,
and Directions
In this, the literature on graph neural network-based recommender systems focuses on their
development, motivation, challenges, graph construction, embedding propagation/aggregation,
model optimization, and computation efficiency. It discusses open problems and promising future
directions in this area, highlighting the importance of graph neural networks in providing information
services on the internet.
A recommender system is a filtering system that presents personalized information to users,
improving user experience and promoting business profit. It evolved through shallow models, neural
models, and GNN-based models. Early models struggled with complex user behaviors and data input.
Recent advances in graph neural networks offer a fundamental opportunity to address these issues,
making GNN-based methods state-of-the-art approaches in recommender systems.
To apply graph neural networks into recommender systems, the critical challenges must be
addressed.
1) First, the data input of the recommender system should be carefully and properly
constructed into a graph, with nodes representing elements and edges representing
relations.
2) Second, for the specific task, the components in graph neural networks should be
adaptively designed, including how to propagate and aggregate, in which existing works
have explored various choices with different advantages and disadvantages.
3) Third, optimizing the GNN-based model, including the optimization goal, loss function, data
sampling, etc., should be consistent with the task requirement. Last, since recommender
systems have strict limitations on the computation cost, and also due to GNNs’ embedding
propagation operations introducing a number of computations, the efficient deployment of
graph neural networks in recommender systems is another critical challenge.
==========================================================================
PAPER 2: ." In Advanced Information Networking and Applications: Proceedings of the 33rd
International Conference on Advanced Information Networking and Applications (AINA-2019) 33,
pp. 895-903. Springer International Publishing, 2020.
Lin, Chu-Hsing, and Hsuan Chi. "A novel movie recommendation system based on
collaborative filtering and neural networks.
Explanation: It aims to identify a set of standards that may be applied to current recommendation
systems and are accurate, reasonable, and practicable. In particular, Scikit-learn and TensorFlow are
two of the prominent technologies that we examine and experiment with. The trials compare these
tools' benefits, error measures, and processing times. We also suggest a novel recommendation
method based on collaborative filtering and neural networks that retains a reduced error measure in
light of the experimental findings.
==========================================================================
PAPER 3: Afoudi, Yassine, Mohamed Lazaar, and MohammedAl Achhab. "Hybrid recommendation
system combined content-based filtering and collaborative prediction using artificial neural
network." Simulation Modelling Practice and Theory 113 (2021): 102375.
Explanation: Using three models—collaborative filtering, content-based, and CF with a selforganizing map model that takes the age demographic characteristic into account—we present a
novel hybrid model of a movie recommendation framework in this research. Our system's key
benefits come from combining all of the model ratings and taking use of each one's strengths. Even
while our method requires more suggestion time speed than the other models, it performs and is
precise.
==========================================================================
PAPER 4: Jena, Kalyan Kumar, Sourav Kumar Bhoi, Chittaranjan Mallick, Soumya Ranjan Jena,
Raghvendra Kumar, Hoang Viet Long, and Nguyen Thi Kim Son. "Neural model based collaborative
filtering for movie recommendation system." International Journal of Information Technology 14, no.
4 (2022): 2067-2077.
Explanation: The neural networks used to create recommendation systems may also be utilised as
auto encoders in a variety of industries. The neural network is made up of several layers, and each
layer is made up of numerous perceptrons that serve as weights. The weights of each perception are
optimised and modified as the network is trained. The goal of this study is to construct a simple
neural network model capable of making very accurate recommendation predictions. The Movie-lens
archive donated the dataset utilised in this recommendations model. The most crucial component of
the model is manipulating the data into the appropriate form and format. Python is used for all of
the work since it has numerous built- in helpful libraries, allowing for easy experimentation and
evaluation
==========================================================================
PAPER 5: Bizimis, Michael. "Hybrid Recommendation Systems using Neural Networks." (2023).
Explanation: In this, they had combined the Neural Collaborative Filtering framework with contentbased methods for item and user profiles, in order to acquire a hybrid recommendation system
based on neural networks. This hybrid recommendation system is still mostly a Collaborative Filtering
approach, as the training objective is that of Collaborative Filtering. Nevertheless, by incorporating
content- based profiles for items and users we are able to avoid the cold-start problem, for which
Collaborative Filtering methods are infamous, as well as leverage potential patterns in the content to
achieve better performance.
==========================================================================