Abstract: In an example embodiment, a generator model such as a large language model (LLM) is leveraged to generate embeddings for both pieces of content and users. The embeddings map the pieces of content and the users into the same latent n-dimensional space. The embeddings are then fine-tuned using a two-tower deep neural network, with one of the towers representing users and the other tower representing content. The two-tower deep neural network is trained to optimize the embeddings over some shared goal, such as user engagement with content, and uses information such as user interactions with content in that process. A clustering technique, such as K-nearest neighbor (kNN) can then be used to identify a grouping of top user/content pairs based on similarity between users and content, as reflected in the embeddings. For a given piece of content, therefore, the top users from that cluster can then be recommended as an audience for the content.

Abstract: In an example embodiment, a generator model such as a large language model (LLM) is leveraged to generate embeddings for both pieces of content and users. The embeddings map the pieces of content and the users into the same latent n-dimensional space. The embeddings are then fine-tuned using a two-tower deep neural network, with one of the towers representing users and the other tower representing content. The two-tower deep neural network is trained to optimize the embeddings over some shared goal, such as user engagement with content, and uses information such as user interactions with content in that process. A clustering technique, such as K-nearest neighbor (kNN) can then be used to identify a grouping of top user/content pairs based on similarity between users and content, as reflected in the embeddings. For a given piece of content, therefore, the top users from that cluster can then be recommended as an audience for the content.

Abstract: In an example embodiment, a generator model such as a large language model (LLM) is leveraged to generate embeddings for both pieces of content and users. The embeddings map the pieces of content and the users into the same latent n-dimensional space. The embeddings are then fine-tuned using a two-tower deep neural network, with one of the towers representing users and the other tower representing content. The two-tower deep neural network is trained to optimize the embeddings over some shared goal, such as user engagement with content, and uses information such as user interactions with content in that process. A clustering technique, such as K-nearest neighbor (kNN) can then be used to identify a grouping of top user/content pairs based on similarity between users and content, as reflected in the embeddings. For a given piece of content, therefore, the top users from that cluster can then be recommended as an audience for the content.