The Matter of Streaming Recommendation Distrust
The algorithm said you would enjoy this. You watched twelve minutes. The algorithm updated its model. It now recommends more things like the thing you watched for twelve minutes.
Official Application for Recommendation System Recalibration Submitted on Behalf of: A User Who Watched Twelve Minutes
Statement of the Matter
The undersigned has used a streaming platform for approximately twenty-two months. During this period, the platform has offered recommendations based on viewing history. The undersigned has found these recommendations progressively less aligned with stated preferences.
This application requests formal review.
Evidence of Deterioration
In month one, recommendations matched viewing history at a rate the undersigned found satisfactory. In months two through six, recommendations remained acceptable with minor anomalies. By month twelve, the top row of recommendations consisted primarily of content the undersigned had started and abandoned.
The abandonment signal was interpreted as engagement. The system does not appear to have a mechanism for distinguishing engagement from abandonment.
The Forensic Concern
The algorithm cannot distinguish between a thing you stopped because you disliked it and a thing you stopped because you had to answer the door. Both produce a partial watch record. Both update the model in the same direction. The person who answers the door and returns to find the content less interesting than remembered generates the same signal as the person who answers the door and does not return at all.
Neither signal is useful for recommendation purposes.
Observation on Positive Feedback
Completing a film produces a strong positive signal. The system recommends additional films of the same type. If the completed film was watched because nothing better was available, the recommendations that follow are recommendations of content suitable for moments when nothing better is available.
The bar is calibrated to the lowest acceptable choice, not to the preferred choice.
The Governing Contradiction
Trust, once extended to a recommendation system, produces data that confirms the system deserves trust, regardless of whether the recommendations improve. The act of watching trains the algorithm. The algorithm produces recommendations. The recommendations are watched. The algorithm is trained further.
The system is self-validating in a way that requires no accuracy.
The Absurd Rule
Several platforms have introduced thumbs-up and thumbs-down feedback mechanisms to supplement behavioral data. Research indicates users provide this feedback in approximately three percent of sessions. The algorithm weights explicit feedback below implicit behavioral data.
Telling the system what you want counts for less than watching what it suggests.
Request
The undersigned requests that the recommendation system be adjusted to treat abandoned content as negative signal and that partial watches under fifteen percent completion be excluded from preference modeling.
The undersigned acknowledges this request will not be reviewed.