Name
Title | ||
|---|---|---|
Model-Based Collaborative Personalized Recommendation on Signed Social Rating Networks. |
Abstract | ||
|---|---|---|
Recommendation on signed social rating networks is studied through an innovative approach. Bayesian probabilistic modeling is used to postulate a realistic generative process, wherein user and item interactions are explained by latent factors, whose relevance varies within the underlying network organization into user communities and item groups. Approximate posterior inference captures distrust propagation and drives Gibbs sampling to allow rating and (dis)trust prediction for recommendation along with the unsupervised exploratory analysis of network organization. Comparative experiments reveal the superiority of our approach in rating and link prediction on Epinions and Ciao, besides community quality and recommendation sensitivity to network organization. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2934681 | ACM Trans. Internet Techn. |
Keywords | Field | DocType |
Link prediction,rating prediction,mixed-membership block modeling | Data mining,Computer science,Inference,Artificial intelligence,Distrust,Probabilistic logic,Generative grammar,Machine learning,Gibbs sampling,Bayesian probability | Journal |
Volume | Issue | ISSN |
16 | 3 | 1533-5399 |
Citations | PageRank | References |
10 | 0.50 | 58 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gianni Costa | 1 | 235 | 24.04 |
| Riccardo Ortale | 2 | 282 | 27.46 |