Knowledge Graph-based Recommender System using Skip-gram Network
Ab Jabar, Rabiatul Adawiyah Binti
Ab Jabar, Rabiatul Adawiyah Binti
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Thesis
Date
2023
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Knowledge Graph-based Recommender System using Skip-gram Network proposes an approach to generate item sequences for training a recommender system using a Knowledge Graph Skip-Gram (KGSG) model. The KGSG model extracts data from multiple sources, converts them into a knowledge graph, and implements biased random walk to generate item sequences. By controlling the balance between exploration and exploitation, the biased random walk produces a sequence of nodes that is well-suited for a specific application or use case. These generated sequences are used to create both positive and negative training examples, which are utilized to train a skip-gram model. The resulting items' embeddings can then be used to make personalized recommendations for new items to users based on their preferences and past viewing history. The proposed KGSG model leverages the power of knowledge graphs and skip-gram network to build a highly accurate and efficient recommender system.
Citation
R.A.B. Ab Jabar, "Knowledge Graph-based Recommender System using Skip-gram Network", M.S. Thesis, Natural Language Processing, MBZUAI, Abu Dhabi, UAE, 2023.