Graph Embedding Based Hybrid Social Recommendation System
This work addresses item recommendation for users by leveraging social graphs, but it is incremental as it combines existing embedding methods without introducing a fundamentally new approach.
The paper tackled item recommendation by applying graph embedding methods to social graphs and proposed a hybrid model combining chosen embeddings, finding that the hybrid model outperformed individual embedding baselines.
Item recommendation tasks are a widely studied topic. Recent developments in deep learning and spectral methods paved a path towards efficient graph embedding techniques. But little research has been done on applying these graph embedding to social graphs for recommendation tasks. This paper focuses at performance of various embedding methods applied on social graphs for the task of item recommendation. Additionally, a hybrid model is proposed wherein chosen embedding models are combined together to give a collective output. We put forward the hypothesis that such a hybrid model would perform better than individual embedding for recommendation task. With recommendation using individual embedding as a baseline, performance for hybrid model for the same task is evaluated and compared. Standard metrics are used for qualitative comparison. It is found that the proposed hybrid model outperforms the baseline.