Name
Abstract | ||
|---|---|---|
Social networks enable users to create different types of personal items. In dealing with serious information overload, the major problems of social recommendation are sparsity and cold start. In existing approaches, relational and heterogeneous domains can not be effectively utilized for social recommendation, which brings a challenge to model users and multiple types of items together on social networks. In this paper, we consider how to represent social networks with multiple relational domains and alleviate the major problems in an individual domain by transferring knowledge from other domains. We propose a novel Hybrid Random Walk (HRW), which can integrate multiple heterogeneous domains including directed/undirected links, signed/unsigned links and within-domain/cross-domain links into a star-structured hybrid graph with user graph at the center. We perform random walk until convergence and use the steady state distribution for recommendation. We conduct experiments on a real social network dataset and show that our method can significantly outperform existing social recommendation approaches. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2396761.2398448 | CIKM |
Keywords | Field | DocType |
social network,multiple relational domain,heterogeneous domain,multiple heterogeneous domain,major problem,multiple type,social recommendation,social recommendation approach,real social network dataset,star-structured hybrid graph,transfer learning | Convergence (routing),Data mining,Graph,Information overload,Social network,Information retrieval,Random walk,Computer science,Transfer of learning | Conference |
Citations | PageRank | References |
42 | 1.16 | 27 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Jiang | 1 | 706 | 47.57 |
| Peng Cui | 2 | 2317 | 110.00 |
| Fei Wang | 3 | 2139 | 135.03 |
| Qiang Yang | 4 | 17039 | 875.69 |
| Wenwu Zhu | 5 | 4399 | 300.42 |
| Shiqiang Yang | 6 | 2478 | 155.24 |