我考虑使用AWS个性化或任何类似的托管推荐服务。
我的问题是，是否有可能根据项目功能获得培训数据中未看到的项目的建议/排名。我看到AWS个性化确实具有项目功能数据集，但是当我阅读有关排名配方的文档时，它明确地说，在任何排名结束时都会添加未培训中的项目。当然 - 新项目没有交互数据，因此仅依赖相互作用数据的任何配方/算法与我的情况无关。
我的问题是，如果可能的话，我是否可以将AWS个性化适用于我的用例，或者您是否知道可以处理它的任何推荐服务。
I consider using aws personalize, or any similar managed recommendation service.
My question is whether it is possible to get recommendations/rankings on items that were not seen in the training data, based on item features. I see that aws personalize does have item feature dataset, but when I read the documentation about ranking recipe it specifically says that items not in the training are added at the end of any ranking. of course - new items have no interaction data, so any recipe/algorithm that solely relies on interaction data is not relevant for my case.
My question is, whether and how can I utilize aws personalize to my use case, if at all possible, or whether you know of any recommender service that can handle it.
发布评论
评论（1）
是的。有特定的亚马逊个性化食谱旨在支持冷启动项目，其中冷项目是交互数据集中没有行为数据的，但项目数据集中的项目元数据。
explorationGeight 推理超参数进行创建个性化广告系列或批处理推理作业。请参阅此博客文章有关详细信息。
探索还适用于为您提供顶级精选 VOD推荐和为您推荐 E-Commerce推荐。您指定
explorationWeight。sellice-items 食谱支持相关项目用例，并希望根据行为数据和项目之间的主题相似性来平衡推荐类似项目。但是，您目前无法通过此食谱控制加权。请参阅此博客文章有关详细信息。 VOD建议提供了类似的功能。
Yes. There are specific Amazon Personalize recipes designed to support cold starting items where a cold item is one without behavioral data in the interactions dataset but with item metadata in the items dataset.
The User-Personalization recipe supports cold starting items through a feature called exploration. You control how much exploration (i.e., recommending cold items) is done with the
explorationWeightinference hyperparameter when creating a Personalize campaign or batch inference job. See this blog post for details.Exploration also applies to domain recommenders for the Top picks for you VOD recommender and Recommended for you e-commerce recommender. You specify the
explorationWeightwhen creating a recommender.The Similar-Items recipe supports the related items use case and looks to balance recommending similar items based on behavioral data and thematic similarity between items. You currently cannot control the weighting with this recipe, though. See this blog post for details. The More like X VOD recommender provides similar functionality.