Explore Further @ Bing is a webpage-to-webpage recommendation product, enhancing the search experience on Bing by surfacing engaging webpage recommendations tied to the search result URLs. In this paper, we present our approach for leveraging Large Language Models (LLMs) for enhancing our web-scale recommendation system. We describe the development and validation of our LLM-powered recommendation quality metric RecoDCG. We discuss our core techniques for utilizing LLMs to make our ranking stage quality-aware. Furthermore, we detail Q' recall, a recall path that enhances our system's candidate generation stage by leveraging LLMs to produce complementary and engaging recommendation candidates. We also address how we optimize our system for multiple objectives, balancing recommendation quality with click metrics. We deploy our work to production, achieving a significant improvement in recommendation quality. We share results from offline and online experiments as well as insights and steps we took to ensure our approaches scale effectively for our web-scale needs.

Modern artificial intelligence (AI) methods have been used to solve problems that many humans struggle to solve. This opens up new opportunities for knowledge discovery and education. We demonstrate ALLURE, an educational AI system for learning to solve the Rubik’s cube that is designed to help students improve their problem solving skills. ALLURE can both find and explain its own strategies for solving the Rubik’s cube as well as build on user-provided strategies. Collaboration between AI and user happens using visual and natural language modalities.

We propose a method for achieving a novel concise graph-based representation for retrieval of objects from large video data. The emphasis in this paper is towards achieving a compact representation of video data for faster retrieval. Specifically, we use information available from scripts and subtitles in order to group all occurrences of an object in video data, which provides a separate representation for each scene. Further, based on the premise that the number of objects in a shot are typically much less than the number of video frames in that shot, we propose a graph-based representation in which vertices represent objects rather than video frames. Key advantages of the proposed approach include faster retrieval, efficiency in performing tasks such as spatial re-ranking and graph partitioning and a single representation for both retrieval and summarization applications. We demonstrate efficacy of the proposed approach in retrieval and summarization applications over video data consisting of episodes of a popular TV series Friends.