rsdiv 0.1.8

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Rsdiv: Diversity improvement framework for recommender systems

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

	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'])

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

• compatible with Optuna.

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