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The VOD recommender for "Top picks for you" uses the same underlying HRNN-based algorithm noted in the StackOverflow answer you linked. Therefore the answer still applies with respect to weighting event types. That is, Personalize does not support weighting specific event types or specific interactions more than others. Instead, the Top picks for you recommender (as well as the underlying user-personalization recipe) builds sequence models from user sessions which are used to learn each user's interest based on a sequence of events rather than specific event types.
Given your event taxonomy, including interactions for SHARE, LIKE, and WATCH_COMPLETE in your interactions dataset are good choices since they indicate positive intent by the user. It may make sense to include WATCH_PARTIAL interactions as well (particularly if they represent the user watching the majority of the content, there is not a subsequent WATCH_COMPLETE for the user for the video, and/or you do not have a sufficient number of WATCH_COMPLETE events across your user base). Otherwise, use WATCH_COMPLETE. If using one of the VOD recommenders, you will need to map your WATCH_COMPLETE events to the required Watch
type and you could map the STARTED events to View
. The SKIP events could be used as impressions if they can be correlated to a WATCH_COMPLETE or WATCH_PARTIAL event for a video that the user eventually watched (e.g., the user skips through the first 3 videos in a sequence and watches the 4th video could be expressed in a PutEvents call with the 4 videos as impressions and the 4th video as the ItemId
that the user Watch
ed).
I suggest not basing the choice of whether to use Personalize or a custom SageMaker model on whether event type weighting is supported. Rather, the choice should be based on the approach that drives the most impact to your business metric (CTR, watch time, etc) with online testing.
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