<ul><li>Traditional collaborative filtering struggles with cold-start issues when no behavioral data exists for new items.</li><li>Using NLP, Sentence-BERT embeddings of movie descriptions are applied for semantic similarity-based recommendations in a Netflix-like dataset.</li><li>The NLP-powered model achieved a Precision@1 of 0.63, with top recommendations often matching the genre of the target item.</li><li>NLP-based content understanding offers a viable cold-start solution for top-1 or top-2 recommendations, showing potential for enhancement in hybrid systems.</li></ul>

From Silence to Insight: How NLP Powers Recommendations for Cold-Start Items

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