# zhougr1993/DeepInterestNetwork

Repository: https://github.com/zhougr1993/DeepInterestNetwork
Canonical: https://ross.abutalabs.com/products/deepinterestnetwork
Language: Python
License Family: other
Last push: 2020-06-12T15:35:36+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3159, "days_push": 2273, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1733, forks 554 (observed 2026-08-28T04:05:28.940551+00:00)

## What it is
A TensorFlow demo implementation of the Deep Interest Network (DIN) paper for click-through rate prediction, run on the Amazon electronics dataset. It also includes baseline competitor models (Wide&Deep, PNN, DeepFM), though the authors recommend their newer DIEN implementation instead.

## Use cases
- reproduce DIN paper results on Amazon data
- implement click-through rate prediction with deep learning
- compare DIN against PNN, DeepFM, and Wide&Deep baselines
- learn how attention-based user interest modeling works
- benchmark CTR models with GAUC metrics

## When to choose
- you need a reference implementation of the DIN paper
- you want to experiment with attention mechanisms for CTR prediction
- you need baseline CTR models for comparison on the Amazon dataset

## When to avoid
- you want production-quality code - the authors state the code quality is poor
- you want the latest results - use the DIEN implementation instead
- you lack a GPU with at least 10GB memory
- you need a maintained library - last release was 2020 and it targets old TensorFlow versions

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning
- platform: python
- tags: ctr-prediction, recommendation, tensorflow, deep-interest-network, research-code, amazon-dataset, algorithms

## Member repositories
- zhougr1993/DeepInterestNetwork (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:28.940551+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:31:29.050280+00:00, confidence not recorded.
  - readme: https://github.com/zhougr1993/DeepInterestNetwork (fetched 2026-08-28T04:05:28.940551+00:00, sha c0c29e91a1a4)
- Data as of 2026-08-30T08:39:29.467469+00:00.