def top_matches(prefs, person, n=5):
""" Rank movie critics matches for `person` from `perfs`.
:param prefs: dict - critics as defined in data.py
:param person: str - movie critic's name to compare others to
:param n: int - number of movie critics to rank
:return: list[tuple(float, str)] - ranking of (score, critic's name)
"""
svd = dt.train_svd(prefs)
result = svd.similar(person, n)
return dt.format_data(result)
def get_recommendations(prefs, person):
""" Recommend movies to `person` matching others' ratings with his.
:param prefs: dict - critics as defined in data.py
:param person: str - movie critic's name to advise
:return: list[tuple(float, str)] - ranking of (score, film's title)
Note that in this function, only movie which user has not rated
will be recommended
"""
n = 3
svd = dt.train_svd(prefs)
result = svd.recommend(person, n, only_unknowns=True, is_row=True)
return dt.format_data(result)
def similarity(prefs, person1, person2):
""" Compute how similar two movie critics are in their tastes,
based on their critics.
You do this by comparing each person with every other person
and calculating a similarity score.
:param prefs: dict - critics as defined in data.py
:param person1: str - movie critic's name
:param person2: str - movie critic's name
:return: float - similarity score
"""
svd = dt.train_svd(prefs)
similarity_score = svd.similarity(person2, person1)
return similarity_score