🤯 Did You Know (click to read)
BERT can calculate semantic similarity between user preferences and content for accurate recommendations.
BERT embeddings allow recommendation engines to match user queries with semantically relevant content. By encoding both user input and document or item descriptions, BERT provides similarity scores that improve personalized suggestions, supporting applications in e-commerce, media streaming, and knowledge platforms.
💥 Impact (click to read)
Semantic recommendations increase user engagement, satisfaction, and discovery of relevant content, enhancing experience across digital platforms.
For users, suggestions feel tailored and contextually appropriate. The irony is that similarity is statistical, not cognitive.
Source
Devlin et al., 2018, BERT: Pre-training of Deep Bidirectional Transformers
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