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Learning expressive representations for high-dimensional yet sparse featureshas been a longstanding problem in information retrieval. Though recent deeplearning methods can partially solve the problem, they often fail to handle thenumerous sparse features, particularly those tail feature values withinfrequent occurrences in the training data. Worse still, existing methodscannot explicitly leverage the correlations among different instances to helpfurther improve the representation learning on sparse features since suchrelational prior knowledge is not provided. To address these challenges, inthis paper, we tackle the problem of representation learning on feature-sparsedata from a graph learning perspective. Specifically, we propose to model thesparse features of different instances using hypergraphs where each noderepresents a data instance and each hyperedge denotes a distinct feature value.By passing messages on the constructed hypergraphs based on our HypergraphTransformer (HyperFormer), the learned feature representations capture not onlythe correlations among different instances but also the correlations amongfeatures. Our experiments demonstrate that the proposed approach caneffectively improve feature representation learning on sparse features.

HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer