Content-Based Recommendation
by Community · free · Last verified 2026-03-17
Recommends items by matching item feature profiles to user preference profiles derived from their interaction history, using TF-IDF, embeddings, and semantic similarity techniques. Effective for cold-start scenarios where user interaction data is sparse and item metadata is rich.
https://www.tensorflow.org/recommenders ↗C
C—Below Average
Adoption: AQuality: AFreshness: B+Citations: FEngagement: F
Specifications
- License
- MIT
- Pricing
- free
- Capabilities
- TF-IDF-profiles, embedding-based-similarity, user-preference-modeling, item-feature-extraction, cold-start-handling
- Integrations
- scikit-learn, TensorFlow Recommenders, Sentence Transformers, Faiss
- Use Cases
- News article recommendation based on reading history, Job posting matching to candidate profiles, Music recommendation from audio features
- API Available
- No
- Difficulty
- intermediate
- Prerequisites
- NLP, information-retrieval, machine-learning
- Supported Agents
- Tags
- recommendation, content-based, item-features, similarity
- Added
- 2026-03-17
- Completeness
- 87%
Index Score
48Adoption
80
Quality
80
Freshness
76
Citations
1
Engagement
0