Calculate implicit rating from streaming behaviour for Recommendation Engine

I have a dataset containing some user streams data for particular videos like below:

u_id|start_stream_time_dt|watch_time_ms|video_category
1|        2021-02-01  | 3600        |  Live


My goal is to build a recommender system for watch streams.
However, I would like to find the optimal watch_stream threshold (or other approaches) that would allow me to define if a user has indeed watched a video because he/she's interested.
In other words I'd like to fill the 1s in the user_item matrix based on these information I have.
Any good approaches to suggest me? or any resources I can look up?
Thanks.