TFIDF_plus_rec.py

#!/usr/bin/python3
# coding=utf-8
"""

@Time    : 下午3:01
@Author  : qcymkxyc
@Email   : qcymkxyc@163.com
@File    : TFIDF_puls_rec.py
@Software: PyCharm

"""
from main.chapter4 import TagBasedTFIDF
from operator import itemgetter
from math import log1p


class TagBasedTFIDFPlus(TagBasedTFIDF):

    def __init__(self):
        TagBasedTFIDF.__init__(self)
        self.item_user_count = dict()

    def _build_matrix(self, train):
        TagBasedTFIDF._build_matrix(self, train)
        for user_id, item_id, tag_id in train:
            self.item_user_count.setdefault(item_id, set())
            self.item_user_count[item_id].add(user_id)

        self.item_user_count = {item_id : len(users) for item_id, users in self.item_user_count.items()}

    def _recommend_user(self, user):
        user_tags = self.user_tag[user]
        recommend_dict = dict()

        user_buys = self.user_item[user]  # 用户已经买过的商品
        for tag_id, tag_count in user_tags.items():
            for item_id, item_count in self.tag_item[tag_id].items():
                # 如果已经买过则不加入推荐名单中
                if item_id in user_buys:
                    continue

                recommend_dict.setdefault(item_id, 0)
                recommend_dict[item_id] += (tag_count * item_count /
                                            (log1p(self.tag_user_count[tag_id]) * log1p(self.item_user_count[item_id])))

        recommends = sorted(recommend_dict.items(), key=itemgetter(1), reverse=True)
        return recommends