Lingjie Weng

Title: Innovations by Lingjie Weng

Introduction

Lingjie Weng is an accomplished inventor based in Sunnyvale, CA. He has made significant contributions to the field of machine learning, particularly in developing systems that enhance user experience in online networks. With a total of 3 patents, Weng's work focuses on creating intelligent recommendations that adapt to user behavior and preferences.

Latest Patents

One of Weng's latest patents involves utilizing machine learning for predicting the next best action. This innovative approach allows for personalized recommendations based on contextual information and user intent. By employing a multitask deep machine learning model, the system can generate action recommendations that are not only tailored to individual users but also coordinate across various aspects of the online network. This flexibility makes the model easy to scale and maintain.

Another notable patent addresses recommendations using session relevance and incremental learning. In this embodiment, a computer system generates recommendations based on user actions performed within a specific timeframe. By classifying user intent and preferences, the system can create diverse recommendations using various models. Additionally, it leverages mini-batches of incoming data to train incremental updates to these recommendation models.

Career Highlights

Lingjie Weng is currently employed at Microsoft Technology Licensing, LLC, where he continues to innovate in the realm of machine learning and user experience. His work has positioned him as a key player in developing advanced recommendation systems that enhance online interactions.

Collaborations

Weng collaborates with talented colleagues, including Rupesh Gupta and Rohan Ramanath, who contribute to the innovative projects at Microsoft Technology Licensing, LLC.

Conclusion

Lingjie Weng's contributions to machine learning and user experience through his patents demonstrate his commitment to innovation. His work not only enhances online interactions but also sets a foundation for future advancements in the field.