Title Details: | |
Graph-based Recommender Systems |
|
Authors: |
Symeonidis, Panagiotis |
Description: | |
Abstract: |
In chapter eight we will study graph-based recommender systems. We will also present similarity measures based on local features of graph (e.g., neighboring nodes of the node under consideration, number of common nodes etc.), as well as based on the characteristics of the overall graph (such as the length of the path connecting two nodes, the number of different paths connecting two nodes etc.). Furthermore, based on local similarity measures we will analyze algorithms such as Common Friends and Preferential Attachment, and we will do the same for algorithms such as Random Walk with Restart (Personalized PageRank), SimRank and PathSim on similarity measures that take into account the overall graph structure though. Finally, we will delve into recommendation systems based on knowledge graphs, i.e., where data is stored using an "ontology" that allows inference following simple rules of description logic.
|
Linguistic Editors: |
Sakellarios, Michalis |
Technical Editors: |
Karatzidis, Dimitrios |
Graphic Editors: |
Symeonidis, Panagiotis |
Type: |
Chapter |
Creation Date: | 29-05-2023 |
Item Details: | |
License: |
Attribution - NonCommercial - ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
Handle | http://hdl.handle.net/11419/9580 |
Bibliographic Reference: | Symeonidis, P. (2023). Graph-based Recommender Systems [Chapter]. In Symeonidis, P. 2023. Intelligent Recommender Systems [Postgraduate textbook]. Kallipos, Open Academic Editions. https://hdl.handle.net/11419/9580 |
Language: |
Greek |
Is Part of: |
Intelligent Recommender Systems |
Publication Origin: |
Kallipos, Open Academic Editions |