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Found 4 papers, 1 papers with code
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations
In practice such ground truth information is rarely available, but we point out that such an inappropriate setting is now ubiquitous in this research area.
Skewness Ranking Optimization for Personalized Recommendation
In this paper, we propose a novel optimization criterion that leverages features of the skew normal distribution to better model the problem of personalized recommendation.
Collaborative Similarity Embedding for Recommender Systems
We present collaborative similarity embedding (CSE), a unified framework that exploits comprehensive collaborative relations available in a user-item bipartite graph for representation learning and recommendation.
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Vertex-Context Sampling for Weighted Network Embedding
Many existing methods adopt a uniform sampling method to reduce learning complexity, but when the network is non-uniform (i. e. a weighted network) such uniform sampling incurs information loss.
