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https://github.com/auxten/go-ctr
Go DeepLearning based Recommendation Framework
https://github.com/auxten/go-ctr
ctr deep-learning golang recommender-system
Last synced: 17 days ago
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Go DeepLearning based Recommendation Framework
- Host: GitHub
- URL: https://github.com/auxten/go-ctr
- Owner: auxten
- License: agpl-3.0
- Created: 2022-06-12T15:09:01.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-12-14T03:31:15.000Z (almost 2 years ago)
- Last Synced: 2024-10-12T10:07:03.575Z (about 1 month ago)
- Topics: ctr, deep-learning, golang, recommender-system
- Language: Go
- Homepage: https://go-ctr.auxten.com/
- Size: 2.79 MB
- Stars: 232
- Watchers: 14
- Forks: 16
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# go-ctr
Recommendation(Click-Through Rate Prediction) Framework for Go, including:
1. Model Training & Prediction
1. Item2vec Embedding
1. Feature-Engineering
1. Common Cost Functions and Metric Functions: AUC, Accuracy, MSE, RMS, Binary Cross Entropy, etc.
1. Common Models
# Models implemented
### [Simple 2 layer MLP](./model/mlp/mlp.go)
GAUC on MovieLens 20M: **0.771258**
- [x] [Simple 2 layer MLP test on MovieLens](./example/movielens/feature_test.go)
- [x] Dropout and L2 regularization
- [x] Batch Normalization
### [YouTube DNN](./model/youtube/dnn.go)
GAUC on MovieLens 20M: **0.760381**
- [x] [YouTube DNN test on MovieLens](./example/movielens/youtube_test.go)
- [x] Dropout and L2 regularization
- [ ] Batch Normalization
### [Deep Interest Network](./model/din/din.go)
GAUC on MovieLens 20M: **0.790542**
- [x] [DIN test on MovieLens](./example/movielens/dinimpl_test.go)
- [x] [Euclidean Distance](model/activation.go) and [Cosine Similarity](model/activation.go) based attention
- [x] Dropout and L2 regularization
- [ ] Batch Normalization
# Demo
You can run the MovieLens training and predict demo by:
```shell
# download and unzip the SQLite DB file
wget https://github.com/auxten/go-ctr/files/9895974/movielens.db.zip && \
unzip movielens.db.zip
# compile the go-ctr and put it in the current directory
GOBIN=`pwd` go install github.com/auxten/go-ctr@latest && \
./go-ctr
```
Wait for the message shown: `Listening and serving HTTP on :8080`.
Then test the API in another terminal:
```shell
curl --header "Content-Type: application/json" \
--request POST \
--data '{"userId":108,"itemIdList":[1,2,39]}' \
http://localhost:8080/api/v1/recommend
```
Should get the response like this:
```json
{"itemScoreList":[
{"itemId":1,"score":0.7517360474797006},
{"itemId":2,"score":0.5240565619788571},
{"itemId":39,"score":0.38496231172036016}
]}
```
So, with a higher score, user #108 may prefer movie #1 over #2 and #39.
# Quick Start
To create a deep learning based recommendation system, you need to follow the steps below:
if you prefer `show me the code`, just go to [MovieLens Example](example/movielens)
1. Implement the `recommend.RecSys` interface including func below:
```golang
GetUserFeature(context.Context, int) (Tensor, error)
GetItemFeature(context.Context, int) (Tensor, error)
SampleGenerator(context.Context) (<-chan Sample, error)
```
2. Call the functions to `Train` and `StartHttpApi`
```golang
model, _ = recommend.Train(recSys)
recommend.StartHttpApi(model, "/api/v1/recommend", ":8080")
```
3. If you want better AUC with item embedding, you can implement the `recommend.ItemEmbedding` interface including func below:
```golang
//ItemEmbedding is an interface used to generate item embedding with item2vec model
//by just providing a behavior based item sequence.
// Example: user liked items sequence, user bought items sequence,
// user viewed items sequence
type ItemEmbedding interface {
ItemSeqGenerator() (<-chan string, error)
}
```
All you need to do is implement the functions of the gray part:
# Docs
For more usage, please refer to the [docs](https://go-ctr.auxten.com/)
# Features
- [x] Pure Golang implementation, battery included.
- [ ] Parameter Server based Online Learning
- [x] Training & Inference all in one binary powered by golang
- Databases support
- [x] MySQL support
- [x] SQLite support
- [ ] Database Aggregation accelerated Feature Normalization
- Feature Engineering
- [x] Item2vec embedding
- [ ] Rule based FE config
- [ ] DeepL based Auto Feature Engineering
- Demo
- [x] MovieLens Demo
# Benchmark
## Embedding
- Apple M1 Max
- Database: SQLite3
- Model: SkipGram, Optimizer: HierarchicalSoftmax
- WindowSize: 5
- Data: [MovieLens 10m](https://grouplens.org/datasets/movielens/10m/)
```
read 9520886 words 12.169282375s
trained 9519544 words 17.155356791s
Search Embedding of:
59784 "Kung Fu Panda (2008)" Action|Animation|Children|Comedy
RANK | WORD | SIMILARITY | TITLE & GENRES
-------+-------+-------------+-------------
1 | 60072 | 0.974392 | Wanted (2008) Action|Thriller
2 | 60040 | 0.974080 | Incredible Hulk, The (2008) Action|Fantasy|Sci-Fi
3 | 60069 | 0.973728 | WALL·E (2008) Adventure|Animation|Children|Comedy|Romance|Sci-Fi
4 | 60074 | 0.970396 | Hancock (2008) Action|Comedy|Drama|Fantasy
5 | 63859 | 0.969845 | Bolt (2008) Action|Adventure|Animation|Children|Comedy
6 | 57640 | 0.969305 | Hellboy II: The Golden Army (2008) Action|Adventure|Comedy|Fantasy|Sci-Fi
7 | 58299 | 0.967733 | Horton Hears a Who! (2008) Adventure|Animation|Children|Comedy
8 | 59037 | 0.966410 | Speed Racer (2008) Action|Adventure|Children
9 | 59315 | 0.964556 | Iron Man (2008) Action|Adventure|Sci-Fi
10 | 58105 | 0.963332 | Spiderwick Chronicles, The (2008) Adventure|Children|Drama|Fantasy
```
## Movie Recommend Performance
- Dataset: MovieLens 100k, split by [80%+20% userId randomly](example/movielens/readme.md)
- Code: [example/movielens](example/movielens)
- Training time: 28s
- AUC: 0.782
# Thanks
To make this project work, quite a lot of code are copied and modified from the following libraries:
- Neural Network & Parameter Server:
- [gorgonia](https://github.com/gorgonia/gorgonia)
- [goro](https://github.com/aunum/goro)
- [go-deep](https://github.com/patrikeh/go-deep)
- [pa-m/sklearn](https://github.com/pa-m/sklearn)
- Feature Engineering:
- [go-featureprocessing](https://github.com/nikolaydubina/go-featureprocessing)
- [featuremill](https://github.com/dustin-decker/featuremill)
- [wego](https://github.com/ynqa/wego)
- FastAPI like framework:
- [go-fastapi](https://github.com/sashabaranov/go-fastapi)
- Gopher logo with [GIMP](https://www.gimp.org/):
- [ashleymcnamara/gophers](https://github.com/ashleymcnamara/gophers)
- [JetBrains](https://www.jetbrains.com/?from=auxten/go-ctr) for providing free license for this project.
# Papers related
- [YouTube DNN](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf)
- [Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/abs/1706.06978)
- [Document Embedding with Paragraph Vectors](https://arxiv.org/abs/1507.07998)