Systems and methods for media discovery

A computing device, comprising:

• one or more processors;
• memory;
• and
• one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs comprising instructions for:
• obtaining a pre-trained recommender model, wherein the pre-trained recommender model is trained using contrastive learning with feature-level augmentation and instance-level augmentation;
• generating, via the pre-trained recommender model, a user embedding based on a plurality of features of the user;
• generating, via the pre-trained recommender model, a respective episode embedding for each episode of a plurality of episodes, each respective episode embedding based on a plurality of features of the corresponding episode;
• generating, via the pre-trained recommender model, a respective similarity score for each episode of a plurality of episodes, the respective similarity score corresponding to a latent similarity between the user embedding and each respective episode embedding;
• ranking the plurality of episodes in accordance with the respective similarity scores;
• and
• recommending a highest ranked episode of the plurality of episodes to the user.

A non-transitory computer-readable storage medium storing one or more programs configured for execution by a computing device having one or more processors and memory, the one or more programs comprising instructions for:

• obtaining a pre-trained recommender model, wherein the pre-trained recommender model is trained using contrastive learning with feature-level augmentation and instance-level augmentation;
• generating, via the pre-trained recommender model, a user embedding based on a plurality of features of the user;
• generating, via the pre-trained recommender model, a respective episode embedding for each episode of a plurality of episodes, each respective episode embedding based on a plurality of features of the corresponding episode;
• generating, via the pre-trained recommender model, a respective similarity score for each episode of a plurality of episodes, the respective similarity score corresponding to a latent similarity between the user embedding and each respective episode embedding;
• ranking the plurality of episodes in accordance with the respective similarity scores;
• and
• recommending a highest ranked episode of the plurality of episodes to the user.