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Rsdiv: Diversity improvement framework for recommender systems

Python PyPI GitHub

rsdiv is a Python package for recommender systems to provide the measurements and improvements for the diversity of results.

Some of its features include:

  • various kinds of metrics to measure the diversity of recommender systems from a quantitative view.
  • various implementations for diversify algorithms and models.
  • various implementations of core recommender algorithms.
  • benchmarks for comparing and further analysis.
  • hyperparameter optimization based on Optuna.

Installation

You can simply install the pre-build binaries with:

$ pip install rsdiv

Or you may want to build from source:

$ cd rsdiv && pip install .

Basic Usage

Prepare for a benchmark dataset

Load a benchmark, say, MovieLens 1M Dataset. This is a table benchmark dataset which contains 1 million ratings from 6000 users on 4000 movies.

>>> import rsdiv as rs
>>> loader = rs.MovieLens1MDownLoader()

Get the user-item interactions (ratings):

>>> ratings = loader.read_ratings()
userId movieId rating timestamp
0 1 1193 5 2000-12-31 22:12:40
1 1 661 3 2000-12-31 22:35:09
... ... ... ... ...
1000207 6040 1096 4 2000-04-26 02:20:48
1000208 6040 1097 4 2000-04-26 02:19:29

Get the users' infomation:

>>> users = loader.read_users()
userId gender age occupation zipcode
0 1 F 1 10 48067
1 2 M 56 16 70072
... ... ... ... ... ...
6038 6039 F 45 0 01060
6039 6040 M 25 6 11106

Get the items' information:

>>> movies = loader.read_items()
movieId title genres release_date
0 1 Toy Story ['Animation', "Children's", 'Comedy'] 1995
1 2 Jumanji ['Adventure', "Children's", 'Fantasy'] 1995
... ... ... ... ...
3881 3951 Two Family House ['Drama'] 2000
3882 3952 Contender, The ['Drama', 'Thriller'] 2000

Evaluate the results in various aspects

Load the evaluator to analyse the results, say, Gini coefficient metric:

>>> metrics = rs.DiversityMetrics()
>>> metrics.gini_coefficient(ratings['movieId'])
>>> 0.6335616301416965

The nested input type (List[List[str]]-like) is also favorable. This is especially usful to evaluate the diversity on topic-scale:

>>> metrics.gini_coefficient(items['genres'])
>>> 0.5158655846858095

Shannon Index and Effective Catalog Size are also available with same usage.

Show the distribution of a given data source

The unbalance of the data distribution can be well illustrated by both barplot and sorted dataframe:

>>> distribution = metrics.get_distribution(items['genres'])

distribution

>>> distribution
category percentage
0 Drama 0.250156
1 Comedy 0.187266
2 Action 0.0784956
... ... ...
16 Western 0.0106117
17 Film-Noir 0.00686642

Draw a Lorenz curve graph for insights

Lorenz curve is a graphical representation of the distribution, the cumulative proportion of species is plotted against the cumulative proportion of individuals. This feature is also supported by rsdiv for helping practitioners' analysis.

metrics.get_lorenz_curve(ratings['movieId'])

Lorenz

Train a recommender

rsdiv provides various implementations of core recommender algorithms. To start with, a wrapper for LightFM is also supported:

>>> rc = rs.FMRecommender(ratings, items, 0.3).fit()

30% of interactions are split for test set, the precision at top 5 can be calculated with:

>>> rc.precision_at_top_k(5)
>>> 0.15639074

the top 100 unseen recommended items for an arbitrary user, say userId: 1024, can be simply given by:

>>> rc.predict_top_n_item(1024, 100)
itemId scores title genres release_date
0 916 1.77356 Roman Holiday ['Comedy', 'Romance'] 1953
1 1296 1.74696 Room with a View ['Drama', 'Romance'] 1986
... ... ... ... ... ...
98 3079 0.371897 Mansfield Park ['Drama'] 1999
99 2570 0.369199 Walk on the Moon ['Drama', 'Romance'] 1999

Improve the diversity

Not only for categorical labels, rsdiv also supports embedding for items, for example, the pretrained 300-dim embedding based on wiki_en by fastText can be simply imported as:

>>> emb = rs.FastTextEmbedder()
>>> emb.embedding_list(['Comedy', 'Romance'])
>>> array([-0.02061814,  0.06264187,  0.00729847, -0.04322025,  0.04619966, ...])

TODO

  • implement the Maximal Marginal Relevance, MMR diversify algorithm
  • implement the Bounded Greedy Selection Strategy, BGS diversify algorithm
  • implement the Determinantal Point Process, DPP diversify algorithm
  • implement the Modified Gram-Schmidt, MGS diversify algorithm

Hyperparameter optimization

TODO

For developers

Contributions welcome! Please contact us.

During your development stage, make sure you have pre-commit installed in your local enviroment:

pip install pre-commit
pre-commit install

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