43 lines
1.8 KiB
Python
43 lines
1.8 KiB
Python
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import numpy as np
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from sklearn.metrics.pairwise import cosine_similarity
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import random
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class Recommender:
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def __init__(self, retriever):
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self.curr_recommendations = []
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self.recommended = []
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self.retriever = retriever
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self.rand_seed = 5
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def _match_tags(self, material, interest):
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score = 0.7
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for tag in material['tags']:
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if cosine_similarity(np.array(self.retriever.encode([tag])),
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np.array(self.retriever.encode([interest]))) > score:
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if material not in self.curr_recommendations:
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self.curr_recommendations.append(material)
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self.recommended.append(False)
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def generate_recommendations(self, interests, new_material):
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for interest in interests:
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for material in new_material:
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self._match_tags(material, interest)
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def make_recommendation(self, user):
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if len(self.curr_recommendations) == 0:
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return ""
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index = random.choice(list(range(0, len(self.curr_recommendations))))
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while self.recommended[index] == True:
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index = random.choice(list(range(0, len(self.curr_recommendations))))
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recommendation = "Hey " + user + "! This " + self.curr_recommendations[index][
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'type'].lower() + " about " + ', '.join(
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self.curr_recommendations[index]['tags']).lower() + " was posted recently by " + \
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self.curr_recommendations[index][
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'author'].lower() + " on the catalogue. You may wanna check it out! It is titled " + \
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self.curr_recommendations[index]['title'].lower() + ". Cheers, Janet"
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# self.curr_recommendations.remove(self.curr_recommendations[index])
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self.recommended[index] = True
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return recommendation
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