Wireframing for Video Streaming Service

Research & assumptions

(based on typical streaming user research patterns and product heuristics)

• Users are more likely to provide reliable feedback after consuming meaningful portions of content.
• Short, structured inputs (stars + tags) increase participation vs. long free-text only flows.
• Community comments drive discovery and confidence but must be moderated for spoilers.
• ML models perform better with graded feedback (star ratings) and categorical metadata (tags).

Design principles guiding decisions

• Meaningful input: Collect signals that are actionable for personalization (stars + tags).
• Protect the experience: Default spoiler protection with user control (blur + toggle).
• Contextual gating: Only allow ratings once the user has meaningful exposure (50% rule) to reduce noisy data.
• Low friction: Make rating fast (required stars + tags) and commenting optional.
• Transparency & control: Let users self-mark spoilers and display AI decisions gently, allowing override.

Feature decisions & rationale

1. 50% watch threshold to unlock rating

Why: Ensures ratings are based on an informed impression rather than a fleeting or partial experience.

Impact: Higher signal quality for recommendation models; reduces false positives/negatives in preference data.

UX tradeoff: Slight friction to early raters vs. better long-term personalization.

2. 1–5 star rating (required)

Why: Granular preference information is more useful to ML than binary likes/dislikes; it captures intensity of preference.

Impact: Enables ranking, weighted aggregation, and fine-grained personalization.

3. Tag (chip) selection (required)

Why: Tags supply categorical reasons for liking/disliking (tone, pacing, acting), useful for interpretable recommendations and facet filtering.

Impact: Improves content matching (e.g., “binge-worthy” signals) and supports UX features like tag-based discovery.

4. Optional free-text comment

Why: Allows qualitative nuance for users who want to elaborate, but remains optional to reduce friction.

Impact: Provides contextual examples and editorial content for the community and could seed editorial highlights.

5. AI spoiler detection + user self-tagging

Why: Automatically protects viewers while keeping authorship agency — users can self-flag, and AI provides a safety net.

Behavior: If AI detects spoilers, prompt the author to edit or confirm publishing; published spoiler comments are blurred by default.

Impact: Balances safety, user freedom, and reduces moderator load.

6. Blur + global spoiler toggle

Why: Default blur protects accidental exposure; a global toggle gives power users control.

Impact: Improves user trust and reduces accidental spoilage.

7. Comments always accessible

Why: Users may want to read others’ impressions before starting an episode or mid-way; unlocking only ratings but keeping comments visible preserves information flow.

Impact: Encourages informed choices and social discovery without compromising data quality.