当LLM推断的用户上下文在生产级流式推荐中增加价值时
When LLM-Inferred User Context Adds Value in Production Streaming Recommendation
- DePaul University（迪波大学）
- Comcast Technology AI（康卡斯特技术AI）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究在生产级流式推荐中评估LLM生成与聚合用户画像，发现其优劣取决于消费模式，建议按模式选择策略。
AI中文摘要：
推荐系统中的上下文信息正从静态、预定义的变量转向从行为中推断出的潜在表示。大语言模型通过将非结构化的交互历史渲染为自然语言摘要来支持这一转变，从而产生一个主题性的用户上下文，该上下文可以被编码并用于替代聚合画像。这种生成的画像在何种条件下优于聚合嵌入，目前主要在领域层面得到了有限的刻画。我们在一个生产级流式平台上评估了语义用户画像策略，并针对完整目录进行排名。评估覆盖了一个2*2的设计空间，交叉了表示类型（聚合或LLM生成）与上下文范围（整体历史或注意力融合的短期和长期上下文）。两种表示类型的相对排序取决于用户的消费模式。在习惯性消费下，聚合画像始终更强，这描述了大约五分之四的人群，而LLM生成的画像对于探索性用户更强，这些用户后续的交互在语义上与其历史有所分歧。我们还观察到LLM生成画像中的流行度吸引子效应，这略微提高了列表内多样性，同时大幅降低了目录覆盖率和减少了新颖性。这些结果表明，一个上下文感知系统可以根据推断的消费模式选择画像策略，而不是对所有用户应用一种表示。
英文摘要：
Contextual information in recommender systems is shifting from static, predefined variables toward latent representations inferred from behavior. Large language models support this shift by rendering an unstructured interaction history as a natural-language summary, which yields a thematic user context that can be encoded and used in place of an aggregate profile. The conditions under which such generated profiles outperform aggregate embeddings have received limited characterization mainly at the domain level. We evaluate semantic user-profiling strategies on a production streaming platform, ranking against the full catalog. The evaluation covers a 2*2 design space crossing representation type (aggregate or LLM-generated) with contextual scope (holistic history or attention-fused short-term and long-term contexts). The relative ordering of the two representation types is conditional on the user's consumption regime. Aggregate profiles are consistently stronger under habitual consumption, which characterizes approximately four-fifths of the population, while LLM-generated profiles are stronger for exploratory users whose subsequent interactions diverge semantically from their history. We also observe a popularity-attractor effect in LLM-generated profiles, which modestly raises within-list diversity while substantially lowering catalog coverage and reducing novelty. These results indicate that a context-aware system can select a profiling strategy from the inferred consumption regime rather than applying one representation to all users.
