feat: Add new layers and models for enhanced recommendation capabilities

README.md

@@ -279,12 +279,11 @@ KMR is optimized for performance with:
 - **Memory Optimization**: Careful memory management in complex layers
 - **Batch Processing**: Optimized for batch operations
 
-## 🔮 Roadmap
+## 💬 Join Our Community
 
-- [ ] **v0.3.0**: Additional model architectures and pre-trained models
-- [ ] **v0.4.0**: Integration with popular ML frameworks
-- [ ] **v0.5.0**: Model zoo with pre-trained weights
-- [ ] **v1.0.0**: Production-ready with comprehensive benchmarks
+Have questions or want to connect with other KDP users? Join us on Discord:
+
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-7289DA?logo=discord&logoColor=white)](https://discord.gg/bhvGunkF)
 
 ## 📄 License
 
@@ -301,6 +300,7 @@ This project is licensed under the MIT License - see the [LICENSE](LICENSE) file
 - **Issues**: [GitHub Issues](https://github.com/UnicoLab/keras-model-registry/issues)
 - **Discussions**: [GitHub Discussions](https://github.com/UnicoLab/keras-model-registry/discussions)
 - **Documentation**: [Online Docs](https://unicolab.github.io/keras-model-registry/)
+- **Discort**: [![Discord](https://img.shields.io/badge/Discord-Join%20Us-7289DA?logo=discord&logoColor=white)](https://discord.gg/bhvGunkF)
 
 ---
 

docs/api/layers.md

@@ -281,3 +281,75 @@ Detects anomalies in numerical features using statistical methods.
 Detects anomalies in categorical features.
 
 ::: kmr.layers.CategoricalAnomalyDetectionLayer
+
+## 📊 Recommendation Systems
+
+### 👥 CollaborativeUserItemEmbedding
+Dual embedding lookup layer for collaborative filtering with separate user and item embedding tables.
+
+::: kmr.layers.CollaborativeUserItemEmbedding
+
+### 🏢 DeepFeatureTower
+Dense neural network tower for processing user or item features in two-tower recommendation architectures.
+
+::: kmr.layers.DeepFeatureTower
+
+### 📏 NormalizedDotProductSimilarity
+Compute normalized dot product (cosine) similarity between user and item representations.
+
+::: kmr.layers.NormalizedDotProductSimilarity
+
+### 🏆 TopKRecommendationSelector
+Select top-K recommendation items based on scores with efficient heap-based selection.
+
+::: kmr.layers.TopKRecommendationSelector
+
+### 🔢 DynamicBatchIndexGenerator
+Generate dynamic batch indices for grouping and indexing operations in recommendation systems.
+
+::: kmr.layers.DynamicBatchIndexGenerator
+
+### 📐 TensorDimensionExpander
+Expand tensor dimensions for broadcasting and reshaping operations in recommendation pipelines.
+
+::: kmr.layers.TensorDimensionExpander
+
+### 🎭 ThresholdBasedMasking
+Apply threshold-based masking to filter values in geospatial and recommendation systems.
+
+::: kmr.layers.ThresholdBasedMasking
+
+### 🌍 HaversineGeospatialDistance
+Compute Haversine great-circle distance between geographic coordinates for location-based recommendations.
+
+::: kmr.layers.HaversineGeospatialDistance
+
+### 🗺️ SpatialFeatureClustering
+Cluster spatial features into geographic regions for location-aware recommendation filtering.
+
+::: kmr.layers.SpatialFeatureClustering
+
+### 📍 GeospatialScoreRanking
+Rank recommendations based on geospatial clustering features for location-aware recommendations.
+
+::: kmr.layers.GeospatialScoreRanking
+
+### 🧠 DeepFeatureRanking
+Deep neural network tower for feature-based ranking in learning-to-rank models.
+
+::: kmr.layers.DeepFeatureRanking
+
+### ⚖️ LearnableWeightedCombination
+Combine multiple recommendation scores with learnable softmax-normalized weights for hybrid recommendations.
+
+::: kmr.layers.LearnableWeightedCombination
+
+### 🔍 CosineSimilarityExplainer
+Compute and explain cosine similarity between embeddings for interpretable recommendations.
+
+::: kmr.layers.CosineSimilarityExplainer
+
+### 💬 FeedbackAdjustmentLayer
+Adjust recommendation scores based on user feedback signals for adaptive recommendations.
+
+::: kmr.layers.FeedbackAdjustmentLayer

docs/api/models.md

@@ -98,6 +98,92 @@ Advanced autoencoder model for anomaly detection with optional preprocessing int
 
 ::: kmr.models.autoencoder.Autoencoder
 
+## 📊 Recommendation Systems
+
+### 🗺️ GeospatialClusteringModel
+Unsupervised geospatial clustering recommendation model using distance-based clustering and spatial ranking.
+
+::: kmr.models.GeospatialClusteringModel
+
+**Key Features:**
+- Haversine distance calculation for geographic coordinates
+- Spatial feature clustering into geographic regions
+- Geospatial score ranking based on proximity
+- Unsupervised learning with entropy and variance losses
+- Configurable training mode (supervised/unsupervised)
+
+### 📈 MatrixFactorizationModel
+Matrix factorization recommendation model using collaborative filtering with user and item embeddings.
+
+::: kmr.models.MatrixFactorizationModel
+
+**Key Features:**
+- Dual user-item embedding lookups
+- Normalized dot product similarity computation
+- Top-K recommendation selection
+- L2 regularization on embeddings
+- Scalable to millions of users/items
+
+### 🏗️ TwoTowerModel
+Two-tower recommendation model with separate towers for user and item features.
+
+::: kmr.models.TwoTowerModel
+
+**Key Features:**
+- Separate deep feature towers for users and items
+- Normalized dot product similarity between towers
+- Content-based feature processing
+- Batch normalization and dropout for regularization
+- Efficient similarity computation
+
+### 🧠 DeepRankingModel
+Deep neural network ranking model for learning-to-rank recommendations.
+
+::: kmr.models.DeepRankingModel
+
+**Key Features:**
+- Deep feature ranking with multiple dense layers
+- Combined user-item feature processing
+- Batch normalization and dropout
+- Learning-to-rank optimization
+- Complex non-linear ranking functions
+
+### 🤝 UnifiedRecommendationModel
+Unified recommendation model combining collaborative filtering, content-based, and hybrid approaches.
+
+::: kmr.models.UnifiedRecommendationModel
+
+**Key Features:**
+- Multiple recommendation components (CF, CB, Hybrid)
+- Score combination with learnable weights
+- Flexible architecture for different data types
+- End-to-end learning of optimal combination
+- Production-ready hybrid system
+
+### 🔍 ExplainableRecommendationModel
+Explainable recommendation model with similarity explanations and feedback adjustment.
+
+::: kmr.models.ExplainableRecommendationModel
+
+**Key Features:**
+- Cosine similarity explanations for transparency
+- User feedback integration
+- Interpretable similarity scores
+- Feedback-aware score adjustment
+- Transparent recommendation reasoning
+
+### 🎯 ExplainableUnifiedRecommendationModel
+Explainable unified recommendation model combining multiple approaches with transparency features.
+
+::: kmr.models.ExplainableUnifiedRecommendationModel
+
+**Key Features:**
+- Multiple recommendation components with explanations
+- Component-level similarity scores
+- Transparent weight learning
+- Explainable hybrid recommendations
+- Full interpretability across all components
+
 ## 🔧 Base Classes
 
 ### 🏛️ BaseModel

docs/index.md

@@ -9,7 +9,7 @@
 </div>
 
 !!! success "🎯 Production-Ready Tabular AI"
-    Build sophisticated tabular models with **38+ specialized layers**, **smart preprocessing**, and **intelligent feature engineering** - all designed exclusively for Keras 3.
+    Build sophisticated tabular models with **50+ specialized layers**, **smart preprocessing**, **intelligent feature engineering**, and **recommendation systems** - all designed exclusively for Keras 3.
 
 ---
 
@@ -20,7 +20,8 @@ KMR (Keras Model Registry) is a comprehensive collection of **production-ready l
 - 🧠 **Advanced Attention Mechanisms** for tabular data
 - 🔧 **Feature Engineering Layers** for data preprocessing  
 - 🏗️ **Pre-built Models** for common ML tasks
-- 📊 **Data Analysis Tools** for intelligent layer recommendations
+- 📊 **Recommendation Systems** with collaborative filtering, content-based, and geospatial models
+- 📈 **Data Analysis Tools** for intelligent layer recommendations
 - ⚡ **Keras 3 Native** - No TensorFlow dependencies in production code
 
 !!! tip "Why KMR?"
@@ -33,9 +34,9 @@ KMR (Keras Model Registry) is a comprehensive collection of **production-ready l
 
 <div class="grid cards" markdown>
 
-- **38+ Production Layers**
+- **50+ Production Layers**
 
-    Advanced attention mechanisms, feature processing, and specialized architectures ready for production use.
+    Advanced attention mechanisms, feature processing, recommendation systems, and specialized architectures ready for production use.
 
     [Explore All Layers →](api/layers.md){ .md-button .md-button--primary }
 
@@ -198,29 +199,31 @@ KMR (Keras Model Registry) is a comprehensive collection of **production-ready l
 
 === "🛒 E-commerce Recommendations"
 
-    Build user-item interaction models:
+    Build recommendation systems with collaborative filtering and content-based features:
 
     ```python
-    from kmr.layers import TabularAttention, GatedFeatureFusion
-
-    user_features = keras.Input(shape=(20,))
-    item_features = keras.Input(shape=(15,))
-
-    user_repr = TabularAttention(
-        num_heads=4, head_dim=16
-    )(keras.layers.Concatenate()(
-        [user_features, item_features]
-    ))
-
-    fused = GatedFeatureFusion()([user_repr, item_features])
-    compatibility = keras.layers.Dense(1)(fused)
-    model = keras.Model(
-        inputs=[user_features, item_features],
-        outputs=compatibility
+    from kmr.models import MatrixFactorizationModel, TwoTowerModel
+
+    # Option 1: Collaborative Filtering
+    model = MatrixFactorizationModel(
+        num_users=10000,
+        num_items=5000,
+        embedding_dim=64,
+        top_k=10
     )
+
+    # Option 2: Content-Based (Two-Tower)
+    model = TwoTowerModel(
+        user_feature_dim=20,
+        item_feature_dim=15,
+        output_dim=64,
+        top_k=10
+    )
+
+    model.compile(optimizer='adam', loss='binary_crossentropy')
     ```
 
-    **Use case:** Product recommendations, CTR prediction, customer lifetime value
+    **Use case:** Product recommendations, CTR prediction, customer lifetime value, personalized search
 
 ---