用大模型把时间地点转成有实际意义的推荐上下文
From Time and Place to Preference: LLM-Driven Geo-Temporal Context in Recommendations
- 用大模型从时间地点生成包含节日、季节等真实场景的嵌入向量
- 在三个数据集上验证，新嵌入能有效提升推荐效果
- 适合想提升推荐系统情境感知能力的研究者和工程师
大多数推荐系统将时间戳视为数值或周期性变量，忽略了节假日、活动和季节性模式等真实世界上下文。本文提出一个可扩展框架，利用大语言模型（LLM）仅通过时间戳和粗粒度位置生成地理时空嵌入，捕捉节假日、季节趋势及本地/全球事件。我们引入一种轻量级的地理时空嵌入信息量测试，在MovieLens、LastFM和一个生产数据集上证明，这些嵌入提供的预测信号与完整模型集成结果一致。地理时空嵌入通过两种方式融入序列模型：(1) 与元数据嵌入直接特征融合；(2) 使用辅助损失强制语义与地理时空对齐。研究强调了采用自适应或混合推荐策略的必要性，并发布了一个增强上下文的MovieLens数据集以支持未来研究。
原文摘要 · Abstract (English)
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.
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