56 lines
2.6 KiB
Python
56 lines
2.6 KiB
Python
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 _new(self, username, material):
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if username not in curr_recommendations:
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return True
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for row in self.curr_recommendations[username]:
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if row['id'] == material['id'] and row['type'] == material['type']:
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return False
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return True
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def _match_tags(self, username, 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 self._new(username, material):
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print('hi')
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self.curr_recommendations[username] = self.curr_recommendations[username].append(material) if username not in self.curr_recommendations else [material]
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self.recommended[username] = self.recommended[username].append(False) if username not in self.recommended else [False]
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def generate_recommendations(self, username, interests, new_material):
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for interest in interests:
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for i, material in new_material.iterrows():
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self._match_tags(username, material, interest)
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def make_recommendation(self, username, name):
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if len(self.curr_recommendations[username]) == 0:
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return ""
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to_consider = [idx for idx, value in enumerate(self.recommended[username]) if value == False]
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if len(to_consider) == 0:
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return ""
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index = random.choice(list(range(0, len(to_consider))))
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index = self.recommended[username][index]
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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 " + name + "! This " + self.curr_recommendations[username][index][
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'type'].lower() + " about " + ', '.join(
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self.curr_recommendations[username][index]['tags']).lower() + " was posted recently by " + \
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self.curr_recommendations[username][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[username][index]['title'].lower() + ". Cheers, Janet"
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# self.curr_recommendations.remove(self.curr_recommendations[index])
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self.recommended[username][index] = True
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return recommendation
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