Editing Recommendation Systems (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Recommender Approach Comparison
! Approach !! Cold Start !! Scalability !! Side Features || Real-Time Update
|-
| User-based CF || Poor || Poor || No || No
|-
| Matrix factorization (ALS) || Poor || Good || No || Batch
|-
| Two-tower neural || Moderate || Excellent || Yes || Near-real-time
|-
| Content-based || Good || Good || Yes || Real-time
|-
| Sequential (BERT4Rec) || Poor || Moderate || Limited || Near-real-time
|-
| Hybrid || Good || Good || Yes || Near-real-time
|}

'''Failure modes''': Filter bubbles — users receive increasingly narrow recommendations, reducing exposure to diverse content. Popularity bias — popular items are over-recommended because they have more interaction data. Feedback loops — recommending an item increases its interactions, making it more recommendable, amplifying initial biases. Engagement optimization can conflict with user wellbeing (recommending addictive content).
</div>

<div style="background-color: #483D8B; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">