document: Learning multiple graphs for document recommendations The Web offers rich relational data with different semantics . In this paper , we address the problem of document recommendation in a digital library , where the documents in question are networked by citations and are associated with other entities by various relations . Due to the sparsity of a single graph and noise in graph construction , we propose a new method for combining multiple graphs to measure document similarities , where different factorization strategies are used based on the nature of different graphs . In particular , the new method seeks a single low-dimensional embedding of documents that captures their relative similarities in a latent space . Based on the obtained embedding , a new recommendation framework is developed using semi-supervised learning on graphs . In addition , we address the scalability issue and propose an incremental algorithm . The new incremental method significantly improves the efficiency by calculating the embedding for new incoming documents only . The new batch and incremental methods are evaluated on two real world datasets prepared from CiteSeer . Experiments demonstrate significant quality improvement for our batch method and significant efficiency improvement with tolerable quality loss for our incremental method .
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