Recommender systems focus on timestamps as numeric or cyclical values, often ignoring real-world context like seasons, holidays and events. We present a scalable framework that utilizes large language models (LLMs) to create geo-temporal embeddings from timestamps and coarse locations, capturing holidays, seasonal trends, and local/global events. We then introduce a geo-temporal embedding informativeness test as a lightweight diagnostic, demonstrating on MovieLens, LastFM, and a large-scale production dataset that these embeddings provide a predictive signal consistent with the outcomes of full model integrations. Geo-temporal embeddings were integrated into sequential models via feature fusion with metadata. Our findings underscore the importance of adaptive and hybrid strategies for improving recommendations. We also release a context-enriched MovieLens dataset. https://github.com/yejinjennyK/movielens-1m-geo-temporal-context https://huggingface.co/datasets/yejinjennyK/movielens-1m-geo-temporal-context.

Millions of people rely on search functionality to find and explore content on entertainment platforms. Modern search systems use a combination of candidate generation and ranking approaches, with advanced methods leveraging deep learning and LLM-based techniques to retrieve, generate, and categorize search results. Despite these advancements, search algorithms can still surface inappropriate or irrelevant content due to factors like model unpredictability, metadata errors, or overlooked design flaws. Such issues can misalign with product goals and user expectations, potentially harming user trust and business outcomes. In this work, we introduce an additional monitoring layer using Large Language Models (LLMs) to enhance content moderation. This additional layer flags content if the user did not intend to search for it. This approach serves as a baseline for product quality assurance, with collected feedback used to refine the initial retrieval mechanisms of the search model, ensuring a safer and more reliable user experience.

At Xfinity, an instant search system provides a variety of results for a given query from different sources. For each keystroke, new results are rendered on screen to the user, which could contain movies, television series, sporting events, music videos, news clips, person pages, and other result types. Users are also able to use the Xfinity Voice Remote to submit longer queries, some of which are more open-ended. Examples of queries include incomplete words which match multiple results through lexical matching (i.e., "ali"), topical searches ("vampire movies"), and more specific longer searches ("Movies with Adam Sandler"). Since results can be based on lexical matches, semantic matches, item-to-item similarity matches, or a variety of business logic driven sources, a key challenge is how to combine results into a single list. To accomplish this, we propose merging the lists via a Learning to Rank (LTR) neural model which takes into account the search query. This combined list can be personalized via a second LTR neural model with knowledge of the user's search history and metadata of the programs. Because instant search is under-represented in the literature, we present our learnings from research to aid other practitioners.