Name
Abstract | ||
|---|---|---|
Traditional tensor factorization based context-aware collaborative filtering considers the context as homogeneous ones, which uses vectorization to implement the factorization as the single context version while ignoring many structural interactions between the heterogeneous contexts. However, cross media data in digital libraries have common and distinctive context, which can be used to discover the latent structural grouping semantics to improve the diversity of recommendation. In this paper, we propose a structural context-aware feature selection framework for cross media recommendation. Firstly, the TUCKER based tensor factorization is conducted on the N-dimensional user-item-content tensor. Then the hidden structural representation are defined as the solution of the structural sparse coding with the loss function by regularizing the terms according to some principle context components, which are optimally selected by the structural grouping sparsity (MtBGS) method. Finally, the top n items with the highest n prediction probabilities are recommended for specific user. Experiments conducted on a cross media dataset based on Douban.com show the effectiveness of diversity for cross media recommendation. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34778-8_74 | PCM |
Keywords | Field | DocType |
structural context-aware cross media,cross media,structural context-aware feature selection,distinctive context,structural grouping sparsity,latent structural grouping semantics,cross media recommendation,cross media data,structural interaction,structural sparse,hidden structural representation,feature selection | Data mining,Collaborative filtering,Pattern recognition,Tensor,Feature selection,Neural coding,Computer science,Vectorization (mathematics),Factorization,Artificial intelligence,Digital library,Semantics | Conference |
Citations | PageRank | References |
1 | 0.36 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenming Yuan | 1 | 34 | 6.71 |
| Kai Yu | 2 | 1 | 0.70 |
| Jia Zhang | 3 | 9 | 2.24 |
| Hong Pan | 4 | 1 | 0.36 |