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.

翻译：工业视频点播（VOD）推荐系统需要更丰富的内容理解，但LLM作为重排序器的设计对每个请求重复执行提示词构建、令牌生成、模型调用、输出解析和备选处理。在高并发延迟敏感型服务中，这些请求时操作复杂化了吞吐量规划、尾延迟控制、容量隔离和可预测运维。本文提出Ocean4Rec重排序层，该层仅离线使用LLM从内容元数据中物化物品OCEAN画像。物品被映射为开放性、尽责性、外向性、宜人性、神经质五个维度的分值，用户画像则通过最近点击和深度链接物品的时域衰减聚合在同一五维空间中构建。在请求时，Ocean4Rec连接预计算的物品画像、用户画像、基础推荐器分值及目录新鲜度，无需LLM调用即可执行数值型重排序。在匿名化的三星智能电视VOD日志上，基于同候选集的Top1000时序留出法离线评估表明：Ocean4Rec对NCF生成器在NDCG@20指标上较更强的非OCEAN基线+新鲜度排序提升7.6%，对LightGCN生成器提升61.5%。HR@20对NCF未呈现显著差异，但对LightGCN提升67.3%，这反映了稀疏的精确物品回放标签以及新鲜度作为工业基线的强度。该结果应被视为有限辅助内容品味特征的离线回放证据，该特征保留了免请求时LLM服务路径的可部署性优势。