Xuejun Liao

Title: Innovations of Xuejun Liao

Introduction

Xuejun Liao is a notable inventor based in Cary, North Carolina, recognized for his contributions to the field of technology and computing. He holds a total of four patents, showcasing his innovative approach to solving complex problems in the realm of recommendation systems.

Latest Patents

One of Liao's latest patents is titled "Multi-thread distributed training of a recommender model." This invention involves a system, method, and computer-program product that receives input comprising multiple pre-defined factor matrices and an implicit feedback dataset. The process includes distributing this input across a controller node and several worker nodes in a distributed computing environment. The model is trained using these nodes, which involves initializing user and item parameters, broadcasting them, and executing training algorithms until a termination condition is met.

Another significant patent is the "Method and system for obtaining item-based recommendations." This invention allows a computing device to obtain a training dataset related to historic user inputs associated with preferences for various services or items. The device trains multiple models using this dataset to generate recommendations based on user preferences and relevancy scores.

Career Highlights

Xuejun Liao is currently employed at SAS Institute Inc., a leading analytics software company. His work focuses on developing advanced algorithms and systems that enhance the capabilities of recommendation engines, making them more efficient and user-friendly.

Collaborations

Liao collaborates with several talented individuals in his field, including Patrick Nathan Koch and Shunping Huang. These collaborations contribute to the innovative environment at SAS Institute Inc., fostering the development of cutting-edge technologies.

Conclusion

Xuejun Liao's work exemplifies the spirit of innovation in technology, particularly in the area of recommendation systems. His patents reflect a deep understanding of complex algorithms and their practical applications, making significant contributions to the field.