Generalized Latent Factor Models for Social Network Analysis

By Wu-Jun Li, Dit-Yan Yeung, Zhihua Zhang. 2011

40 citations recorded by the source corpus.

Subfields: Recommender Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Recommender Systems

1. Dit-Yan Yeung
2. Hao Wang
3. Miao Zhao
4. Hongwei Wang
5. Naiyan Wang
6. Minyi Guo
7. James She
8. Xiaopeng Li

Citations in the corpus

• Collaborative Deep Learning for Recommender Systems (2014, methodology): “Hybrid methods [1, 18, 12] seek to get the best of both worlds by combining content-based and CF-based methods.”
• Joint Topic-Semantic-aware Social Recommendation for Online Voting (2017, methodology): “Hybrid methods [9, 16] combine content-based and collaborative filtering models in many hybridization approaches, such as weighted, switching, cascade and feature combination or augmentation.”
• Collaborative Variational Autoencoder for Recommender Systems (2017, methodology): “Latent factor models have been proposed as such tightly coupled methods like [2, 14, 24], where auxilliary information is processed and regarded as a feature for the collaborative methods.”