Polara is the first recommendation framework that allows a deeper analysis of recommender systems performance, based on the idea of feedback polarity (by analogy with sentiment polarity in NLP).
In addition to standard question of "how good a recommender system is at recommending relevant items", it allows assessing the ability of a recommender system to avoid irrelevant recommendations (thus, less likely to disappoint a user). You can read more about this idea in a research paper Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks. The research results can be easily reproduced with this framework, visit a "fixed state" version of the code at https://github.com/Evfro/fifty-shades (there're also many usage examples). The framework also features efficient tensor-based implementation of an algorithm, proposed in the paper, that takes full advantage of the polarity-based formulation.
Current version of Polara supports both Python 2 and Python 3 environments. Future versions are likely to drop support of Python 2 to make a better use of Python 3 features.
The framework heavily depends on Pandas, Numpy, Scipy and Numba packages. Better performance can be achieved with mkl (optional). It's also recommended to use jupyter notebook for experimentation. Visualization of results can be done with help of matplotlib. The easiest way to get all those at once is to use the latest Anaconda distribution.
If you use a separate conda environment for testing, the following command can be issued to ensure that all required dependencies are in place (see this for more info):
conda install --file conda_req.txt
Alternatively, a new conda environment with all required packages can be created by:
conda create -n <your_environment_name> python=3.7 --file conda_req.txt
The easiest way is to install directly from source. Activate your conda environment and run:
pip install --no-cache-dir --upgrade git+https://github.com/evfro/polara.git#egg=polara
This will install the current release version. For the most recent developer version insert @develop between polara.git and #egg=polara in the line above.
Alternatively, you can manually clone this repository to a local machine (git clone git://github.com/evfro/polara.git). Once in the root of the newly created local repository, run
python setup.py install
A special effort was made to make a recsys for humans, which stresses on the ease of use of the framework. For example, that's how you build a pure SVD recommender on top of the Movielens 1M dataset:
from polara.recommender.data import RecommenderData
from polara.recommender.models import SVDModel
from polara.datasets.movielens import get_movielens_data
# get data and convert it into appropriate format
ml_data = get_movielens_data(get_genres=False)
data_model = RecommenderData(ml_data, 'userid', 'movieid', 'rating')
# build PureSVD model and evaluate it
svd = SVDModel(data_model)
svd.build()
svd.evaluate()Several different scenarios and use cases, which cover many practical aspects, can also be found in the examples directory.
Basic models can be extended by subclassing RecommenderModel class and defining two required methods: self.build() and self.get_recommendations(). Here's an example of a simple item-to-item recommender model:
from polara.recommender.models import RecommenderModel
class CooccurrenceModel(RecommenderModel):
def __init__(self, *args, **kwargs):
super(CooccurrenceModel, self).__init__(*args, **kwargs)
self.method = 'item-to-item' # pick some meaningful name
def build(self):
# build model - calculate item-to-item matrix
user_item_matrix = self.get_training_matrix()
# rating matrix product R^T R gives cooccurrences count
i2i_matrix = user_item_matrix.T.dot(user_item_matrix) # gives CSC format
# exclude "self-links" and ensure only non-zero elements are stored
i2i_matrix.setdiag(0)
i2i_matrix.eliminate_zeros()
# store matrix for generating recommendations
self.i2i_matrix = i2i_matrix
def get_recommendations(self):
# get test users information and generate top-k recommendations
test_matrix, test_data = self.get_test_matrix()
# calculate predicted scores
i2i_scores = test_matrix.dot(self.i2i_matrix)
# prevent seen items from appearing in recommendations
if self.filter_seen:
self.downvote_seen_items(i2i_scores, test_data)
# generate top-k recommendations for every test user
top_recs = self.get_topk_elements(i2i_scores)
return top_recsAnd the model is ready for evaluation:
i2i = CooccurrenceModel(data_model)
i2i.build()
i2i.evaluate()Here's an example of how to perform top-k recommendations experiments with 5-fold cross-validation for several models at once:
from polara.evaluation import evaluation_engine as ee
from polara.recommender.models import PopularityModel, RandomModel
# define models
i2i = CooccurrenceModel(data_model)
svd = SVDModel(data_model)
popular = PopularityModel(data_model)
random = RandomModel(data_model)
models = [i2i, svd, popular, random]
metrics = ['ranking', 'relevance'] # metrics for evaluation: NDGC, Precision, Recall, etc.
folds = [1, 2, 3, 4, 5] # use all 5 folds for cross-validation (default)
topk_values = [1, 5, 10, 20, 50] # values of k to experiment with
# run 5-fold CV experiment
result = ee.run_cv_experiment(models, folds, metrics,
fold_experiment=ee.topk_test,
topk_list=topk_values)
# calculate average values across all folds for e.g. relevance metrics
scores = result.mean(axis=0, level=['top-n', 'model']) # use .std instead of .mean for standard deviation
scores.xs('recall', level='metric', axis=1).unstack('model')which results in something like:
| model | MP | PureSVD | RND | item-to-item |
|---|---|---|---|---|
| top-n | ||||
| 1 | 0.017828 | 0.079428 | 0.000055 | 0.024673 |
| 5 | 0.086604 | 0.219408 | 0.001104 | 0.126013 |
| 10 | 0.138546 | 0.300658 | 0.001987 | 0.202134 |
| ... | ... | ... | ... | ... |
Polara by default takes care of raw data and helps to organize full evaluation pipeline, that includes splitting data into training, test and evaluation datasets, performing cross-validation and gathering results. However, if you need more control on that workflow, you can easily implement your custom usage scenario for you own needs.
If you simply want to build a model on a provided data, then you only need to define a training set. This can be easily achieved with the help of prepare_training_only method (assuming you have a pandas dataframe named train_data with corresponding "user", "item" and "rating" columns):
data_model = RecommenderData(train_data, 'user', 'item', 'rating')
data_model.prepare_training_only()Now you are ready to build your models (as in examples above) and export them to whatever workflow you currently have.
By default polara makes testset and trainset disjoint by users, which allows to evaluate models against user warm-start.
However in some situations (for example, when polara is used within a larger pipeline) you might want to implement strictly a known user scenario to assess the quality of your recommender system on the unseen (held-out) items for the known users. The change between these two scenarios as controlled by setting data_model.warm_start attribute to True or False. See Warm-start and standard scenarios Jupyter notebook as an example.
If you don't want polara to perform data splitting (for example, when your test data is already provided), you can use the set_test_data method of a RecommenderData instance. It has a number of input arguments that cover all major cases of externally provided data. For example, assuming that you have new users' preferences encoded in the unseen_data dataframe and the corresponding held-out preferences in the holdout dataframe, the following command allows to include them into the data model:
data_model.set_test_data(testset=unseen_data, holdout=holdout, warm_start=True)Polara will automatically perform all required transformations to ensure correct functioning of the evaluation pipeline. To evaluate models you simply call standard methods without any modifications:
svd.build()
svd.evaluate()In this case the recommendations are generated based on the testset and evaluated against the holdout. See more usage examples in the Custom evaluation notebook.
Polara offers even more options to highly customize experimentation pipeline and tailor it to specific needs. See, for example, Reproducing EIGENREC results notebook to learn how Polara can be used to reproduce experiments from the "EIGENREC: generalizing PureSVD for effective and efficient top-N recommendations" paper.
If you find this framework useful for your research, please cite the following paper:
"HybridSVD: when collaborative information is not enough"; Evgeny Frolov and Ivan Oseledets, 2019. In Proceedings of the 13th ACM Conference on Recommender Systems (RecSys '19). ACM, New York, NY, USA, 331-339.
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