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name: sequence-models description: "Operate DeepCTR DIN, DIEN, DSIN, and BST sequence/session recommendation workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

DeepCTR Sequence Models

Use this sub-skill when a task involves DeepCTR's sequence/session recommender models: DIN, BST, DIEN, or DSIN. It is specialized for input naming, sequence/session feature construction, attention/GRU caveats, and tiny synthetic smoke checks.

DeepCTR facts to preserve in downstream work: DeepCTR is a TensorFlow/Keras CTR/recommender package imported as deepctr; this skill targets DeepCTR 0.9.4. TensorFlow is a separate dependency. Use public tensorflow.keras APIs in new code.

Start Here

1. Read references/sequence-inputs.md for exact input dictionaries, hist_/sess_ field names, padding, seq_length/sess_length, and embedding sharing examples.
2. Read references/api-reference.md for model constructor signatures and version-sensitive parameters.
3. Read references/troubleshooting.md when a sequence model builds with wrong history behavior, has missing length inputs, fails on Dice/GRU/session shapes, or produces masking surprises.
4. Use scripts/sequence_tiny_smoke.py for a bundled synthetic DIN/BST/DSIN build-fit-predict smoke check that does not require external data.

Model Selection Router

• Use DIN when each row has a candidate item/category and one flat padded history sequence; attention matches candidate behavior features to hist_ history fields.
• Use BST when the same DIN-style hist_ history should be processed through Transformer layers before attention; include seq_length.
• Use DIEN when the task is interest evolution over a history sequence, especially GRU variants or auxiliary negative-sampling loss; treat full DIEN training as legacy/version-sensitive.
• Use DSIN when history is already split into multiple sessions; the model does not split raw event streams and requires sess_<index>_<feature> fields plus sess_length.

Boundary Routes

• For generic SparseFeat, DenseFeat, VarLenSparseFeat, hashing, weighted sequences, or feature validation unrelated to DIN/BST/DIEN/DSIN naming, route to data-and-feature-columns.
• For model compile, fit, callbacks, save/load, custom objects, prediction, and TensorFlow environment management, route to keras-model-workflows.
• For multitask learning and TensorFlow Estimator workflows, route to their separate DeepCTR skill areas.

Critical Checks Before Building

• DIN/BST/DIEN: every behavior feature name in history_feature_list must have a matching history field named exactly hist_ + feature name, for example item_id -> hist_item_id.
• BST and DIEN require a seq_length input; set length_name="seq_length" on history VarLenSparseFeat columns and include x["seq_length"].
• DSIN: for every session index 0..sess_max_count-1 and every behavior feature, include sess_<index>_<feature> and include x["sess_length"].
• Use 0 only for padding in behavior sequence/session id arrays; encode valid behavior ids from 1 upward and set vocabulary_size >= max_id + 1.
• Share candidate/history embeddings with embedding_name, for example SparseFeat("hist_item_id", ..., embedding_name="item_id"), and keep shared vocabulary size, embedding dimension, and trainability compatible.
• On newer TensorFlow versions, prefer att_activation="sigmoid" for DIN/DIEN smoke/debug runs; dice is the DeepCTR default but more version-sensitive.