用离线大模型生成视频内容人格画像，请求时免调用模型提升推荐效率
Ocean4Rec: Offline LLM-Derived OCEAN Profiles for Request-Time VOD Reranking
- 离线用大模型将视频映射为五维人格特征（OCEAN），避免请求时重复调用模型
- 在三星智能电视数据上，相比基线模型，NDCG@20提升61.5%（LightGCN）
- 适合对延迟敏感、需稳定吞吐的工业级视频推荐系统
工业级视频点播（VOD）推荐需要更丰富的内容理解，但现有大模型作为重排器的设计在每次请求时重复进行提示构造、文本生成、模型调用、输出解析和降级处理。在高并发低延迟场景下，这些操作增加了吞吐量规划、尾部延迟控制、资源隔离和可预测运行的复杂性。本文提出Ocean4Rec，一个仅在离线阶段使用大模型生成项目OCEAN人格画像的重排层。项目被映射为开放性、尽责性、外向性、宜人性和神经质性得分，用户画像则通过近期点击与深度链接项目的加权聚合构建于同一五维空间。请求时，Ocean4Rec结合预计算的项目画像、用户画像、基础推荐分数和目录新鲜度，执行纯数值重排，无需调用大模型。基于匿名三星智能电视VOD日志的同候选集Top1000时间留出测试显示，相较于更强的非OCEAN基线+新鲜度排序，Ocean4Rec在NCF生成器上提升NDCG@20达7.6%，在LightGCN生成器上提升61.5%；HR@20在NCF上不显著，在LightGCN上提升67.3%，反映精确项回放标签稀疏及新鲜度作为工业基线的强大。结果应视为一个辅助内容-偏好特征的离线回放证据，该特征在保留无请求时大模型调用部署优势的同时，实现性能提升。
原文摘要 · Abstract (English)
Industrial video-on-demand (VOD) recommenders need richer content understanding, but LLM-as-reranker designs repeat prompt construction, token generation, model invocation, output parsing, and fallback handling for each request. In high-volume latency-sensitive services, these request-time operations complicate throughput planning, tail-latency control, capacity isolation, and predictable operation. This paper presents Ocean4Rec, a reranking layer that uses an LLM only offline to materialize item OCEAN profiles from content metadata. Items are mapped into Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism scores, while user profiles are built by time-decayed aggregation of recently clicked and deep-linked items in the same five-dimensional space. At request time, Ocean4Rec joins precomputed item profiles, user profiles, base recommender scores, and catalog recency, then performs numeric reranking without an LLM call. On anonymized Samsung Smart TV VOD logs, same-candidate Top1000 temporal-holdout offline evaluations show that Ocean4Rec improves NDCG@20 over a stronger non-OCEAN Base+Recency ordering by 7.6% for an NCF generator and 61.5% for a LightGCN generator. HR@20 is inconclusive for NCF and improves by 67.3% for LightGCN, reflecting sparse exact-item replay labels and the strength of recency as an industrial baseline. The result should be read as offline replay evidence for a bounded auxiliary content-taste feature that preserves the deployability advantage of a request-time-LLM-free serving path.
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