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.
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 explorationWeight inference 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 explorationWeight when 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.