Abstract: Embodiments are disclosed for an audio recommendation system trained to recommend music audio sequences for pairing with query video sequences using neural networks. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including a query video sequence and natural language text. The disclosed systems and methods further comprise generating a fused visual-text embedding based on a visual embedding and a text embedding corresponding to the input. The disclosed systems and methods further comprise comparing audio embeddings for music audio sequences of a music audio sequences database with the fused visual-text embedding. The disclosed systems and methods further comprise determining a music audio sequence from the music audio sequences database as the recommended music audio sequence for pairing with the query video sequence based on a similarity metric calculated between an audio embedding for the music audio sequence and the fused visual-text embedding.

Abstract: A system is provided for use with a data providing portion and first, second, and third user devices. The data providing portion can provide initial data having a plurality of data entries. The system includes a first processing portion that can generate a ranking of the data entries based on a weighting factor; a second processing portion that can provide the first user device with a first level of access and which can generate a second ranking; a third processing portion that can provide the second user device with a second level of access to one of the ranking and the second ranking; and a fourth processing portion that can provide the third user device with a third level of access to one of the ranking and the second ranking.