Interest-based conversational recommendation system

A system, comprising:

• one or more memories;
• and
• at least one processor each coupled to at least one of the memories and configured to perform operations comprising:
• generating a probabilistic pseudo-user neural network model based on at least one interest probability distribution corresponding to a pseudo-user profile;
• training, using the probabilistic pseudo-user neural network model, a conversational recommendation system to learn a recommendation policy, wherein the conversational recommendation system comprises an interest-exploration engine and a prompt-decision engine, and wherein the training includes performing one or more iterations of an iterative learning process, including:
• selecting, by the interest-exploration engine, an interest-exploration strategy based on one or more of the following:
• an interest-exploration policy, an earlier pseudo-user response generated by the probabilistic pseudo-user neural network model, content data, and pseudo-user interaction history;
• selecting, by the prompt-decision engine, an interest prompt based on a prompt-decision policy and the selected interest-exploration strategy;
• generating, by the probabilistic pseudo-user neural network model, another pseudo-user response based on the selected interest prompt;
• updating a reward function, corresponding to the interest-exploration engine and the prompt-decision engine, based on the another pseudo-user response;
• and
• updating, using a reinforcement-learning method, the interest-exploration policy and the prompt-decision policy based on at least the updated reward function;
• and
• generating, using the trained conversational recommendation system, a real-time recommendation having a high play-probability based on a minimal number of iterations of conversation between a user and the trained conversational recommendation system.

A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

• generating, by at least one computer processor, a probabilistic pseudo-user neural network model based on at least one interest probability distribution corresponding to a pseudo-user profile;
• training, using the probabilistic pseudo-user neural network model, a conversational recommendation system to learn a recommendation policy, wherein the conversational recommendation system comprises an interest-exploration engine and a prompt-decision engine, and wherein the training includes performing one or more iterations of an iterative learning process, including:
• selecting, by the interest-exploration engine, an interest-exploration strategy based on one or more of the following:
• an interest-exploration policy, an earlier pseudo-user response generated by the probabilistic pseudo-user neural network model, content data, and pseudo-user interaction history;
• selecting, by the prompt-decision engine, an interest prompt based on a prompt-decision policy and the selected interest-exploration strategy;
• generating, by the probabilistic pseudo-user neural network model, another pseudo-user response based on the selected interest prompt;
• updating a reward function, corresponding to the interest-exploration engine and the prompt-decision engine, based on the another pseudo-user response;
• and
• updating, using a reinforcement-learning method, the interest-exploration policy and the prompt-decision policy based on at least the updated reward function;
• and
• generating, using the trained conversational recommendation system, a real-time recommendation having a high play-probability based on a minimal number of iterations of conversation between a user and the trained conversational recommendation system.