FUSING USER REVIEWS INTO HETEROGENEOUS INFORMATION NETWORK RECOMMENDED
DOI:
https://doie.org/10.5281/8xh64t78Keywords:
Reviews, Heterogeneous, Network,,Abstract
A recommendation system's job is to make educated guesses about user mental processes and to
make educated predictions about user interests. This system may tailor its responses to each individual user,
taking into account their individual goals and preferences. More efficient data analysis is required for
improved suggestion making. Different recommendation systems were developed using diverse methods. As
the number of over-the-top (OTT) platforms, as well as retail, travel, and other types of websites, all of which
seek to provide better suggestions to their customers, grows, so does the interest in studying such systems.
The primary objective of this research is to survey the landscape of recommendation systems and conduct a
comparative analysis of them according to a number of criteria. After looking at a number of articles, we saw
that numerous recommendation systems were developed, most of which relied heavily on more conventional
approaches. However, recently academics and businesses have been interested in knowledge graph-based
recommendation systems due to its ability to solve a wide range of performance and information sparsity
related issues and provide superior suggestions.The system's efficiency is enhanced by combining machine
learning with a knowledge graph. We'll also look at the many algorithms presented in the literature that make
use of a knowledge graph to improve upon the recommendation process.We have also provided a high-level
overview of the system we propose.Finally, we'll offer you some ideas on where the field of recommendation
systems may go from here.
References
In the year 2021, the IEEE Transactions on Knowledge and Data Engineering published an article by
Haithem Mezni, Djamal Benslimane, and Ladjel Bellatreche titled "Context-aware Service Recommendation
based on Knowledge Graph Embedding." The article's DOI is 10.1109/TKDE.2021.3059506.
2019 IEEE International Conference on Services Computing (SCC), DOI: 10.1109/SCC.2019.00041,
Sihang Hu, Zhiying Tu, Zhongjie Wang, "A POISensitive Knowledge Graph based Service Recommendation
Method."
"Contextual Correlation Preserving Multiview Featured Graph Clustering," 2019 IEEE Transaction on
Cybernetics, DOI: 10.1109/ TCYB.2019.2926431, by Tiantian He,Yang Liu,Tobey H. Ko,Keith C. C. Chan,
and YewSoon Ong.
IEEE Access, DOI: 10.1109/ACCESS.2019.2928848 Cairong Yan and Yizhou Chen, "Differentiated
Fashion Recommendation Using Knowledge Graph and Data Augmentation," 2017.
"CASR-TSE: Context-aware Web Services Recommendation for Modelling Weighted Temporal-Spatial
Effectiveness," 2017 IEEE Transactions on Services Computing, DOI: 10.1109/TSC.2017.2782793 Xiaoliang
Fan, Yakun Hu, Zibin Zheng,Yujie Wang, Wenbo Chen, and Patrick Brezillon
2018 IEEE Transactions on Knowledge and Data Engineering, "GMC: Graph-based Multi-view
Clustering," DOI: 10.1109/TKDE.2019.2903810; Hao Wang, Yan Yang, and Bing Liu.
