An efficient PyTorch implementation of the evaluation metrics in recommender systems.

Last update: Dec 02, 2022

Related tags

Deep Learning recsys_metrics

Overview

recsys_metrics

An efficient PyTorch implementation of the evaluation metrics in recommender systems.

Overview • Installation • How to use • Benchmark • Citation •

Overview

Highlights

Efficient (vectorized) implementations over mini-batches
Standard RecSys metrics: precision, recall, map, mrr, hr, ndcg
Beyond-accuracy metrics: e.g. coverage, diversity, novelty, etc.
All metrics support a top-k argument.

Why do we need `recsys_metrics`?

Installation

You can install recsys_metrics from PyPI:

pip install recsys_metrics

Or you can also install the latest version from source:

pip install git+https://github.com/zuoxingdong/[email protected]

Note that we support Python 3.7+ only.

How to use

Let us take Hit Rate (HR) to illustrate how to use this library:

preds = torch.tensor([
    [.5, .3, .1],
    [.3, .4, .5]
])
target = torch.tensor([
    [0, 0, 1],
    [0, 1, 1]
])
hit_rate(preds, target, k=1, reduction='mean')

>> tensor(0.5000)

The first example in the batch does not have a hit (i.e. top-1 item is not a relevant item) and second example in the batch gets a hit (i.e. top-1 item is a relevant item). Thus, we have a hit-rate of 0.5.

The API of other metrics are of the same format.

Benchmark

Metrics	Single Example	Mini-Batch
Precision
Recall
MAP
MRR
HR
NDCG

Citation

This work is inspired by Torchmetrics from PyTorchLightning Team.

Please use this bibtex if you want to cite this repository in your publications:

@misc{recsys_metrics,
      author = {Zuo, Xingdong},
      title = {recsys_metrics: An efficient PyTorch implementation of the evaluation metrics in recommender systems.},
      year = {2021},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/zuoxingdong/recsys_metrics}},
    }

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An efficient PyTorch implementation of the evaluation metrics in recommender systems.

Related tags

Overview

Overview

Highlights

Why do we need recsys_metrics?

Installation

How to use

Benchmark

Citation

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Releases(v0.0.3)

v0.0.3(Aug 1, 2022)

v0.0.2(Jun 14, 2022)

Owner

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