Model Reference

Shaped offers a rich library of model policies that power retrieval, scoring, and ranking within your recommendation and search systems. These policies range from classic collaborative filtering techniques and rule-based heuristics to state-of-the-art deep learning architectures. Explore the available policies below to understand their capabilities and how to configure them for optimal performance.

Embedding models

Embedding models are used to represent items and users in vector space.

🧩 ALS

Matrix Factorization (Alternating Least Squares) for implicit feedback collaborative filtering.

🧩 SVD

Matrix Factorization (Singular Value Decomposition inspired) for collaborative filtering, often including biases.

💡 ELSA

Scalable Linear Shallow Autoencoder for implicit feedback recommendations using factorized structure.

✨ EASE

Embarrassingly Shallow Autoencoder with a simple closed-form solution for item collaborative filtering.

🗼 Two-Tower

Scalable deep learning model with separate user and item networks for efficient candidate retrieval.

🐝 beeFormer

Fine-tunes sentence Transformers on interaction data, bridging semantic & behavioral signals.

🔄 Item2Vec

Learns item embeddings based on co-occurrence within interaction sequences (Word2Vec adaptation).

➡️ SASRec

Self-Attentive Sequential Recommendation model using Transformers for next-item prediction.

↔️ BERT4Rec

Bidirectional Transformer (BERT) for sequential recommendation via masked item prediction.

✅ GSASRec

Improved SASRec (Generalized Self-Attentive) reducing overconfidence in sequential models.

Scoring models

Scoring models are used to rank items by relevance or other predicted business metrics (e.g., engagement, predicted click-through-rate, conversion).

🌳 LightGBM

Efficient Gradient Boosting Decision Tree framework, suitable for ranking (e.g., LambdaMART).

🌳 XGBoost

Scalable Gradient Boosting Decision Tree framework, effective for classification and ranking tasks.

➕ Wide & Deep

Combines a wide linear model (memorization) and a deep neural network (generalization) for scoring.

🔗 DeepFM

Combines Factorization Machines (FM) and Deep Neural Networks (DNN) for interaction modeling.

#️⃣ Ngram

Simple sequential model predicting based on fixed-length interaction counts (n-grams).

📈 Rising Popularity

Trending policy ranking items by recent increase in engagement (momentum).