Abstract: Highlights•A novel multi-modal fusion framework for recommender systems is proposed.•Complementarity between early fusion and late fusion is identified and utilized.•A popularity-aware pruning strategy is proposed to remove the noisy interactions.•Extensive experiments on three datasets demonstrate the superiority of DGHNet.
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