Learning of Multimodal Representations With Random Walks on the Click Graph

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How researchers see this paper

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

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “And there is some work focusing on cross-modal retrieval [33], [34], [36], [99], e.”
A Survey of Multi-View Representation Learning (2016, unknown): “[186] consider learning multi-modal representation from the perspective of encoding the explicit/implicit”
An Overview of Cross-Media Retrieval: Concepts, Methodologies, Benchmarks, and Challenges (2017, methodology): “In addition, in the work of [39], clicks of users are exploited as side information for cross-media common space learning.”
Expert Finding for Community-Based Question Answering via Ranking Metric Network Learning (2016, background): “Wu et al. [Wu et al., 2016] devise the embedding algorithm for multi-modal networks.”
Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning (2017, methodology): “Wu et al. (Wu et al. 2016a) devise the embedding algorithm for multi-modal networks.”