(2019). Efficient and socio-aware recommendation approaches for bigdata networked systems. Institution of Engineering and Technology. https://doi.org/10.1049/PBPC035F_ch4
Περίληψη
In this chapter, we present several approaches designed for providing efficient recommendations in large web systems characterized by bigdata scales. The key feature of the considered approaches is that they all rely on different elements and properties of social/complex network analysis for addressing various deficiencies of legacy and current recommendation systems when very large operational scales emerge. The main challenges of recommendations addressed by the presented approaches are the diversity (novelty) of recommendations, the cold-start problem, scalability and noise filtering issues, as well as the efficiency of developing these approaches and integrating them in operational systems. This chapter aspires to provide an educated overview, leading to a solid fundamental background on how social/complex network analysis can be exploited for more effective recommendations in stringent environments characterized by large scales ofusers, items and associated data, cumulatively referred to as big network data. Furthermore, our work aims at highlighting the design principles that are more interesting for enabling the extension of the presented approaches and their combination with other current state-of-the-art techniques, thus leading to more socioaware and efficient recommendation approaches in the near and longer term future. © The Institution of Engineering and Technology 2020.
- DOI
- 10.1049/PBPC035F_ch4
- ISBN
- 9781785619755
- Τύπος
- Book
- Έτος
- 2019
Σύνδεσμοι
BibTeX
@book{Karyotis201941,
title = {Efficient and socio-aware recommendation approaches for bigdata networked systems},
author = {V. Karyotis and M. Vitoropoulou and N. Kalatzis and I. Roussaki and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099450309&doi=10.1049%2fPBPC035F_ch4&partnerID=40&md5=ae7b353bd55c8bdfc06d9fd8ea9177c1},
doi = {10.1049/PBPC035F_ch4},
isbn = {9781785619755},
year = {2019},
date = {2019-01-01},
journal = {Big Data Recommender Systems: Algorithms, Architectures, Big Data, Security and Trust},
pages = {41-70},
publisher = {Institution of Engineering and Technology},
abstract = {In this chapter, we present several approaches designed for providing efficient recommendations in large web systems characterized by bigdata scales. The key feature of the considered approaches is that they all rely on different elements and properties of social/complex network analysis for addressing various deficiencies of legacy and current recommendation systems when very large operational scales emerge. The main challenges of recommendations addressed by the presented approaches are the diversity (novelty) of recommendations, the cold-start problem, scalability and noise filtering issues, as well as the efficiency of developing these approaches and integrating them in operational systems. This chapter aspires to provide an educated overview, leading to a solid fundamental background on how social/complex network analysis can be exploited for more effective recommendations in stringent environments characterized by large scales ofusers, items and associated data, cumulatively referred to as big network data. Furthermore, our work aims at highlighting the design principles that are more interesting for enabling the extension of the presented approaches and their combination with other current state-of-the-art techniques, thus leading to more socioaware and efficient recommendation approaches in the near and longer term future. © The Institution of Engineering and Technology 2020.},
note = {cited By 5},
keywords = {},
pubstate = {published},
tppubtype = {book}
}