From Time and Place to Preference: LLM-Driven Geo-Temporal Context in Recommendations

2025-10-28

AI Summary

Existing recommender systems often oversimplify timestamps as numerical values or periodic signals, neglecting the dynamic influence of geotemporal context—such as holidays, local events, and seasonal patterns—on user behavior. To address this, we propose a lightweight, LLM-driven spatiotemporal contextual embedding method: leveraging large language models, it jointly processes timestamps and coarse-grained geographic information to generate semantically rich embeddings encoding holiday observances, seasonal trends, and locale-specific events. We design an embedding informativeness metric and integrate it into a sequential recommendation framework, enabling adaptive feature fusion and auxiliary loss optimization. Extensive experiments on multiple benchmark datasets demonstrate significant improvements in recommendation accuracy. Furthermore, we release a high-quality, spatiotemporally enhanced version of the MovieLens dataset to advance research in context-aware recommendation.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Most recommender systems treat timestamps as numeric or cyclical values, overlooking real-world context such as holidays, events, and seasonal patterns. We propose a scalable framework that uses large language models (LLMs) to generate geo-temporal embeddings from only a timestamp and coarse location, 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 production dataset that these embeddings provide predictive signal consistent with the outcomes of full model integrations. Geo-temporal embeddings are incorporated into sequential models through (1) direct feature fusion with metadata embeddings or (2) an auxiliary loss that enforces semantic and geo-temporal alignment. Our findings highlight the need for adaptive or hybrid recommendation strategies, and we release a context-enriched MovieLens dataset to support future research.

Problem

• Recommender systems overlook real-world temporal context like holidays and events
• Existing approaches treat timestamps as numeric values without semantic meaning
• Need to capture geo-temporal patterns for adaptive recommendation strategies

Innovation

• LLMs generate geo-temporal embeddings from timestamp and location
• Embeddings capture holidays, seasonal trends, and local events
• Two integration methods: feature fusion and auxiliary loss alignment