def recommendPosts(dataModel):
model = MatrixPreferenceDataModel(dataModel)
print 'User ID`s: '
print model.user_ids()
## print 'Item ID`s: '
## print model.item_ids()
userID= input('Please enter a userID: ')
print 'Loading recommended posts...'
similarity = UserSimilarity(model, pearson_correlation)
recommender = UserBasedRecommender(model, similarity, with_preference=True)
return recommender.recommend(userID)
def build_and_save_recommendations(self, user_items_preference_map):
model = MatrixPreferenceDataModel(user_items_preference_map)
#item_similarity = ItemSimilarity(model, loglikehood_coefficient)
user_similarity = UserSimilarity(model, loglikehood_coefficient)
item_recommendations = ItemBasedColaborativeFiltering(user_items_preference_map).build_recommendations()
user_recommender = UserBasedRecommender(model, user_similarity, with_preference=True)
for item, item_weight_map in item_recommendations.iteritems():
ItemRecommendations.save_recommendations_for_item(item, item_weight_map.iteritems())
user_work_queue = multiprocessing.Queue()
for user_id in model.user_ids().tolist():
user_work_queue.put(user_id)
ParallelWork(UserRecommenderWorker, (user_work_queue, user_recommender)).begin()