Recommendation system serves as a conduit connecting users to an incredibly large, diverse and ever growing collection of contents. In practice, missing information on fresh (and tail) contents needs to be filled in order for them to be exposed and discovered by their audience. We here share our success stories in building a dedicated fresh content recommendation stack on a large commercial platform. To nominate fresh contents, we built a multi-funnel nomination system that combines (i) a two-tower model with strong generalization power for coverage, and (ii) a sequence model with near real-time update on user feedback for relevance. The multi-funnel setup effectively balances between coverage and relevance. An in-depth study uncovers the relationship between user activity level and their proximity toward fresh contents, which further motivates a contextual multi-funnel setup. Nominated fresh candidates are then scored and ranked by systems considering prediction uncertainty to further bootstrap content with less exposure. We evaluate the benefits of the dedicated fresh content recommendation stack, and the multi-funnel nomination system in particular, through user corpus co-diverted live experiments. We conduct multiple rounds of live experiments on a commercial platform serving billion of users demonstrating efficacy of our proposed methods.

翻译：推荐系统作为连接用户与海量、多样且持续增长内容库的纽带。在实践中，新内容（以及长尾内容）需要填补信息缺失，才能被其目标受众发现和曝光。我们在此分享在大型商业平台上构建专用新内容推荐系统的成功经验。为候选新内容，我们构建了多漏斗候选生成系统，该系统结合了（i）具有强泛化能力以实现覆盖率的双塔模型，以及（ii）基于用户反馈进行近实时更新的序列模型以提升相关性。多漏斗架构有效平衡了覆盖率和相关性。深入研究发现用户活跃度与他们对新内容接近程度之间的关系，这进一步启发了上下文感知的多漏斗架构。候选新内容随后由考虑预测不确定性的系统进行评分和排序，以进一步引导曝光较少的内容。通过用户语料分流在线实验，我们评估了专用新内容推荐系统（尤其是多漏斗候选生成系统）的收益。我们在服务数十亿用户的商业平台上进行了多轮在线实验，验证了所提出方法的有效性。