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Add an option to whether to include previously liked items or not #140

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merged 2 commits into from
Jul 25, 2018
Merged

Add an option to whether to include previously liked items or not #140

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fd984bc
add an option to whether to include previously liked items or not
Jul 15, 2018
c61e2f7
Fix typo
Jul 15, 2018
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6 changes: 4 additions & 2 deletions implicit/nearest_neighbours.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,15 +23,17 @@ def fit(self, weighted):
self.similarity = all_pairs_knn(weighted, self.K, show_progress=self.show_progress).tocsr()
self.scorer = NearestNeighboursScorer(self.similarity)

def recommend(self, userid, user_items, N=10, filter_items=None, recalculate_user=False):
def recommend(self, userid, user_items,
N=10, filter_already_liked_items=True, filter_items=None, recalculate_user=False):
""" returns the best N recommendations for a user given its id"""
# recalculate_user is ignored because this is not a model based algorithm
items = N
if filter_items:
items += len(filter_items)

indices, data = self.scorer.recommend(userid, user_items.indptr, user_items.indices,
user_items.data, K=items)
user_items.data, K=items,
remove_own_likes=filter_already_liked_items)
best = sorted(zip(indices, data), key=lambda x: -x[1])

if not filter_items:
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11 changes: 8 additions & 3 deletions implicit/recommender_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,8 @@ def fit(self, item_users):
pass

@abstractmethod
def recommend(self, userid, user_items, N=10, filter_items=None, recalculate_user=False):
def recommend(self, userid, user_items,
N=10, filter_already_liked_items=True, filter_items=None, recalculate_user=False):
"""
Recommends items for a user

Expand Down Expand Up @@ -120,11 +121,15 @@ def __init__(self):
# cache of item norms (useful for calculating similar items)
self._item_norms = None

def recommend(self, userid, user_items, N=10, filter_items=None, recalculate_user=False):
def recommend(self, userid, user_items,
N=10, filter_already_liked_items=True, filter_items=None, recalculate_user=False):
user = self._user_factor(userid, user_items, recalculate_user)

# calculate the top N items, removing the users own liked items from the results
liked = set(user_items[userid].indices)
if filter_already_liked_items is True:
liked = set(user_items[userid].indices)
else:
liked = set()
scores = self.item_factors.dot(user)
if filter_items:
liked.update(filter_items)
Expand Down