Lilly Tech Systems
Content-Based Recommendation Methods
Content-based methods recommend products based on their attributes rather than user behavior patterns. They solve the cold start problem for new products and provide explainable recommendations like "similar style" or "same brand."
Content-Based Approaches
Attribute Matching
Use structured product attributes (category, brand, price range, color, material) to find similar items. Simple, fast, and highly explainable.
NLP Embeddings
Encode product titles and descriptions using sentence transformers. Find similar products in embedding space for semantically meaningful "similar to" recommendations.
Visual Similarity
Use CNN features (ResNet, CLIP) to find visually similar products. Critical for fashion, home decor, and any category where appearance drives purchase decisions.
Knowledge Graphs
Model product relationships (complements, substitutes, accessories) in a graph structure. Enable "complete the outfit" and "goes well with" recommendations.
Building Content-Based Features
| Feature Type | Source | Embedding Method |
|---|---|---|
| Text | Title, description, reviews | Sentence-BERT, all-MiniLM-L6 |
| Visual | Product images | CLIP, ResNet-50, EfficientNet |
| Categorical | Brand, category, tags | One-hot or learned embeddings |
| Numerical | Price, rating, weight | Normalization, binning |
| Multimodal | Text + image combined | CLIP joint embedding space |
When to Use Content-Based Methods
- New Product Launch: No interaction data exists yet, but product attributes enable immediate recommendations
- Long-Tail Items: Niche products with few interactions benefit from attribute-based similarity over sparse collaborative signals
- Explainable Recommendations: "Similar style" and "same brand" recommendations build user trust through transparency
- Visual-First Categories: Fashion, furniture, and art where visual similarity is a primary purchase driver