Systems and methods for personalized multi-task training for recommender systems

A system for making recommendations, the system comprising:

• a memory that stores a recommendation neural network model and a plurality of processor executable instructions;
• a communication interface that receives time series data indicating a user-item interaction history;
• and
• one or more hardware processors that read and execute the plurality of processor- executable instructions from the memory to perform operations comprising:
• transforming the time series data into a user-item graph;
• encoding, by a neural network encoder, the user-item graph into user embeddings and item embeddings;
• generating a plurality of losses according to a plurality of training tasks performed based on the user embeddings and, item embeddings;
• training the recommendation neural network model by updating the user embeddings and the item embeddings via backpropagation based on a weighted sum of gradients of the plurality of losses;
• and
• generating, by a neural network decoder, one or more recommended items for a given user based on the updated user embeddings and the updated item embeddings.

A non-transitory machine-readable medium comprising a plurality of machine-executable instructions which, when executed by one or more processors, are adapted to cause the one or more processors to perform operations comprising:

• transforming time series data indicating a user-item interaction history into a user- item graph;
• encoding, by a neural network encoder, the user-item graph into user embeddings and item embeddings;
• generating a plurality of losses according to a plurality of training tasks performed based on the user embeddings and, item embeddings;
• training the recommendation neural network model by updating the user embeddings and the item embeddings via backpropagation based on a weighted sum of gradients of the plurality of losses;
• and
• generating, by a neural network decoder, one or more recommended items for a given user based on the updated user embeddings and the updated item embeddings.