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gorse: Go Recommender System Engine

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gorse is an offline recommender system backend based on collaborative filtering written in Go.

This project is aim to provide a high performance, easy-to-use, programming language irrelevant recommender micro-service based on collaborative filtering. We could build a simple recommender system on it, or set up a more sophisticated recommender system using candidates generated by it. It features:

Pipeline: supports data loading, data splitting, model training, model evaluation and model selection.
Tools: provides the data import/export tool, model evaluation tool and RESTful recomender server.
Optimization: accelerates computations by SIMD instructions and multi-threading.

Install

To start using gorse, install Go and run go get:

go get github.com/zhenghaoz/gorse/...

It will download all packages and build the gorse command line into your $GOBIN path.

If your CPU supports AVX2 and FMA3 instructions, use the avx2 build tag to enable AVX2 and FMA3 instructions.

go get -tags='avx2' github.com/zhenghaoz/gorse/...

Usage

gorse is an offline recommender system backend based on collaborative filtering written in Go.

Usage:
  gorse [flags]
  gorse [command]

Available Commands:
  export-feedback Export feedback to CSV
  export-items    Export items to CSV
  help            Help about any command
  import-feedback Import feedback from CSV
  import-items    Import items from CSV
  serve           Start a recommender sever
  test            Test a model by cross validation
  version         Check the version

Flags:
  -h, --help   help for gorse

Use "gorse [command] --help" for more information about a command.

It's easy to setup a recomendation service with gorse.

Step 1: Import feedback and items.

gorse import-feedback ~/.gorse/gorse.db u.data --sep $'\t'
gorse import-items ~/.gorse/gorse.db u.item --sep '|'

It imports feedback and items from CSV files into the database file ~/.gorse/gorse.db. The low level storage engine is implemented by BoltDB. u.data is the CSV file of ratings in MovieLens 100K dataset and u.item is the CSV file of items in MovieLens 100K dataset. All CLI tools are listed in the CLI-Tools section of Wiki.

Step 2: Start a server.

./gorse server -c config.toml

It loads configurations from config.toml and start a recommendation server. It may take a while to generate all recommendations. Detailed information about configuration is in the Configuration section of Wiki. Before set hyper-parameters for the model, it is useful to test the performance of chosen hyper-parameters by the model evaluation tool.

Step 3: Get recommendations.

curl 127.0.0.1:8080/recommends/1?number=5

It requests 5 recommended items for the 1-th user. The response might be:

[
    {
        "ItemId": 202,
        "Score": 2.901297852545712
    },
    {
        "ItemId": 151,
        "Score": 2.8871064286482864
    },
    ...
]

"ItemId" is the ID of the item and "Score" is the score generated by the recommendation model used to rank. See RESTful APIs in Wiki for more information about RESTful APIs.

Document

Visit GoDoc for detailed documentation of codes.
Visit ReadTheDocs for tutorials, examples and usages.

Performance

gorse is much faster than Surprise, and comparable to librec while using less memory space than both of them. The memory efficiency is achieved by sophisticated data structures.

Cross-validation of SVD on MovieLens 100K [Source]:

Cross-validation of SVD on MovieLens 1M [Source]:

Contributors

Any kind of contribution is expected: report a bug, give a advice or even create a pull request.

Acknowledgments

gorse is inspired by following projects:

Limitations

gorse has limitations and might not be applicable to some scenarios:

No Scalability: gorse is a recommendation service on a single host, so it's unable to handle large data.
No Features: gorse exploits interactions between items and users while features of items and users are ignored.

leizhu900516 / gorse