Overview

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.

🧩 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.

🌳 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.

🗼 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.

#️⃣ Ngram

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

🔄 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.

🛍️ Item-Content Similarity

Content-based policy comparing item features to pooled features from user's interaction history.

👤 User-Content Similarity

Content-based policy comparing user profile features to pooled features of item interactors.

🖇️ User-Item Content Similarity

Content-based policy directly comparing embeddings from user attributes and item attributes.

🕒 Chronological

Rule-based policy ranking items by timestamp (newest or oldest first).

⭐ Popular

Rule-based policy ranking items by overall interaction counts (popularity).

🔥 Recently Popular

Trending policy ranking items by popularity with time decay (e.g., HackerNews style).

📈 Rising Popularity

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

🎲 Random

Rule-based policy assigning random scores, useful for baselines or exploration.

✨ Auto-Tune

Automatically finds the best model policy and hyperparameters for your data.

➕ Score Ensemble

Combines ranked lists from multiple scoring policies, often via interleaving.

🚫 No-Operation

Placeholder policy that performs no action, used for testing or disabling stages.