Recommendations intersection metric

[blondered]

Feature Description

Metric to measure intersection in user-item (or item-item) pairs between recommendation lists.

Why this feature?

It helps for both candidate-generators selection in pipelines, and for popularity bias measurement.

Additional context

intersecton = Intersection(k=10)

# one metric
intersecton_value = intersecton.calc(reco=recos, ref_recos=ref_recos)
intersecton_per_user = precision.calc_per_user(reco=recos, ref_recos=ref_recos)

# calc_metrics
calc_metrics(
    metrics = {"precision": precision, "intersection": intersecton},
    reco=recos,
    interactions=df_test,
    ref_recos: Union[pd.Datafame, Dict[Hashable, pd.DataFrame]]=ref_recos
)

We can keep ref_recos a simple pd.DataFrame to calculate intersections with one algorithm.
For multiple intersection calculations we can pass multiple models recommendations in a dict:
ref_recos = {"one": ref_recos_one, "two": ref_recos_two}
Result dict from calc_metrics will have intersection_one and intersection_two keys if ref_recos is a dict (merging keys from metrics and ref_recos.

# cross_validate
cv_results = cross_validate(
    dataset=dataset,
    splitter=splitter,
    models=models,
    metrics=metrics,
    k=10,
    filter_viewed=True,
    ref_models=["one", "two"], # here we just select keys from `models` argument
    validate_ref_models=False  # optionally exclude ref_models from other metrics calculation
)