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I did the amazon personalize deep dive series on youtube. At the timestamp 8:33 in the video, it was mentioned that 'Personalize does not understand negative feedback.' and that any interaction you submit is assumed to be a positive one.

But I think that giving negative feedback could improve the recommendations that we give on a whole. Personalize knowing that a user does not like a given item 'A' would help ensure that it does not recommend items similar to 'A' in the future.

Is there any way in which we can give negative feedback(ex. user doesn't like items x,y,z) to amazon personalize?

A possible way to give negative feedback that I thought of:

Let's say users can give ratings out of 5 for movies. Every time a user gives a rating >= 3 in the interactions dataset, we add an additional interaction in the dataset (i.e we have two interactions saying that a user rated a movie >=3 in the interactions.csv instead of just one). However, if he gives a rating <=2 (meaning he probably doesn't like the movie), we just keep the single interaction of that in the interactions dataset (i.e. we only have one interaction saying that a user rated a movie <=2 in the interactions.csv file)

  • Would this in any way help to convey to personalize that ratings <=2 are not as important/that the user did not like them?
anusha garimella
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Negative feedback, where the user explicitly indicates that they dislike an item, is currently not supported as training input for Amazon Personalize. Furthermore, there is currently no way to add weight/reward to specific interactions by event type or event value (see this answer for details).

With that said, you can use impressions with your interactions to indicate items that were seen by the user but that they chose not to interact with. Impressions are only supported by the User-Personalization recipe. From the docs:

Unlike other recipes, which solely use positive interactions (clicking, watching, or purchasing), the User-Personalization recipe can also use impressions data. Impressions are lists of items that were visible to a user when they interacted with (clicked, watched, purchased, and so on) a particular item.

Using this information, a solution created with the User-Personalization recipe can calculate the suitability of new items based on how frequently an item has been ignored, and change recommendations accordingly. For more information see Impressions data.

Impressions are not the same as an explicitly negative interaction but they do imply that the impressed items were considered less relevant/important to the user than the item that they chose to interact with.

Another approach that can be used to consider negative interactions when making recommendations is to have two event types: one event type for positive intent (e.g., "like", "watch", or "purchase") and one event type for dislike (e.g., "dislike"). Then create a Personalize solution that only trains on the positive event type. Finally, at inference time, use a Personalize filter to exclude items that the user has recently disliked.

EXCLUDE ItemID WHERE Interactions.EVENT_TYPE IN ("dislike")

In this case, Personalize is still training only on positive interactions but with the filter you won't recommend items that the user disliked.

James J
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