---

name: sequence-and-interest-models description: "Use DeepCTR-Torch DIN/DIEN and VarLenSparseFeat sequence workflows for behavior-history CTR inputs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Sequence and interest models

Use this sub-skill for DeepCTR-Torch workflows that model behavior histories or multi-value sequence features, especially DIN, DIEN, and VarLenSparseFeat inputs whose rows contain padded item/category histories.

Route by task

User task	Load
Build or debug a DIN/DIEN behavior-history model	references/din-dien-workflows.md
Check sequence array shapes, padding, length_name, or masks	references/sequence-feature-shapes.md
Look up DIN/DIEN, sequence-pooling, attention, or feature-column parameters	references/api-reference.md
Diagnose behavior-list, embedding, sequence length, negative sampling, hash, or short-sequence failures	references/troubleshooting.md
Run a tiny DIN behavior-history smoke	scripts/din_sequence_smoke.py
Run a tiny multi-value VarLenSparseFeat smoke	scripts/varlen_feature_smoke.py

Core invariants

1. history_feature_list/behavior_feature_list contains target sparse feature names such as item_id and cate_id, not history column names.
2. For every behavior feature x, provide a target SparseFeat(name=x, ...) and a history VarLenSparseFeat whose feature name is exactly hist_ + x.
3. Share target/history embeddings with embedding_name=x on each history SparseFeat; do the same for DIEN negative histories.
4. DIN/DIEN behavior histories should be post-padded to the configured maxlen and accompanied by a length_name such as seq_length.
5. For DIEN with use_negsampling=True, add neg_hist_ + x feature columns and model-input arrays for every behavior feature x.

Boundary routing

• For general SparseFeat, DenseFeat, VarLenSparseFeat constructor basics and non-sequence data validation, use feature-column-inputs.
• For non-sequence single-output model families such as DeepFM, WDL, DCN, AutoInt, or xDeepFM, use single-task-modeling.
• For multi-output MTL classes, use multitask-modeling.
• This sub-skill does not cover custom sequence architectures beyond the package's DIN/DIEN and standard pooled VarLenSparseFeat workflows.