Interest-based conversational recommendation system

US12190864B1 · kind B1 · utility

Assignee

• Roku, Inc. · US

Inventors

• Fei Xiao · Mountain View, US
• Amit Verma · Schweinhub, DE
• Rohit Mahto · San Jose, US
• Rameen Mahdavi · San Francisco, US
• Nam Vo · San Jose, US
• Zidong Wang · San Jose, US
• Lian Liu · Rancho Palos Verdes, US
• Jose Sanchez · Orlando, US
• Pulkit Aggarwal · San Jose, US
• Atishay Jain · Noida, IN
• Abhishek Bambha · Burlingame, US
• Ronica Jethwa · Mountain View, US

Key dates

• Filing date: Jun 5, 2024
• Grant date: Jan 7, 2025
• Expiry date: Jun 5, 2044

Classification

• Technology area (CPC G): Physics
• CPC primary: G10L2015/0635
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Disclosed herein are system, method and/or computer program product embodiments, and/or combinations thereof, for training a conversational recommendation system. An embodiment generates a probabilistic pseudo-user neural network model based on at least one interest probability distribution corresponding to a pseudo-user profile. The embodiment trains, using the pseudo-user neural network model, the conversational recommendation system to learn a recommendation policy, where the conversational recommendation system includes an interest-exploration engine and a prompt-decision engine. The training includes performing an iterative learning process that includes selecting an interest-exploration strategy based on one or more of the following: an interest-exploration policy, an earlier pseudo-user response generated by the pseudo-user neural network model, content data, and pseudo-user interaction history. The embodiment then generates, using the trained conversational recommendation system, a real-time recommendation having high play probability based on the minimal number of iterations of conversation between a user and the trained conversational recommendation system.