Example #1
0
    def calculate_users_similarity(self, user_dictionary, user1, user2):

        common_items = extractor.get_common_items(user_dictionary, user1,
                                                  user2)

        if not common_items:
            return None

        if self._min_common_items is not None and len(
                common_items) < self._min_common_items:
            return None

        user1_overall_ratings = user_dictionary[user1].item_ratings
        user1_multi_ratings = user_dictionary[user1].item_multi_ratings

        user2_overall_ratings = user_dictionary[user1].item_ratings
        user2_multi_ratings = user_dictionary[user2].item_multi_ratings

        similarity_sum = 0.

        for item in common_items:
            user1_item_ratings = list(user1_multi_ratings[item])
            user1_item_ratings.insert(0, user1_overall_ratings[item])
            user2_item_ratings = list(user2_multi_ratings[item])
            user2_item_ratings.insert(0, user2_overall_ratings[item])

            similarity_sum += similarity_calculator.calculate_similarity(
                user1_item_ratings, user2_item_ratings,
                self._similarity_metric)

        similarity = similarity_sum / len(common_items)

        return similarity
    def calculate_users_similarity(self, user_dictionary, user1, user2):

        common_items = extractor.get_common_items(user_dictionary, user1, user2)

        if not common_items:
            return None

        if self._min_common_items is not None and len(
                common_items) < self._min_common_items:
            return None

        user1_overall_ratings = user_dictionary[user1].item_ratings
        user1_multi_ratings = user_dictionary[user1].item_multi_ratings

        user2_overall_ratings = user_dictionary[user1].item_ratings
        user2_multi_ratings = user_dictionary[user2].item_multi_ratings

        similarity_sum = 0.

        for item in common_items:
            user1_item_ratings = list(user1_multi_ratings[item])
            user1_item_ratings.insert(0, user1_overall_ratings[item])
            user2_item_ratings = list(user2_multi_ratings[item])
            user2_item_ratings.insert(0, user2_overall_ratings[item])

            similarity_sum += similarity_calculator.calculate_similarity(
                user1_item_ratings, user2_item_ratings, self._similarity_metric)

        similarity = similarity_sum / len(common_items)

        return similarity
    def calculate_users_similarity(self, user_dictionary, user1, user2):

        common_items = extractor.get_common_items(user_dictionary, user1,
                                                  user2)

        if not common_items:
            return None

        if self._min_common_items is not None and len(
                common_items) < self._min_common_items:
            return None

        user1_overall_ratings =\
            extractor.get_user_ratings(user_dictionary, user1, common_items)
        user1_multi_ratings =\
            extractor.get_user_multi_ratings(user_dictionary, user1, common_items)

        user2_overall_ratings =\
            extractor.get_user_ratings(user_dictionary, user2, common_items)
        user2_multi_ratings =\
            extractor.get_user_multi_ratings(user_dictionary, user2, common_items)

        num_criteria = len(user1_multi_ratings[0])
        total_similarity = 0.

        for i in xrange(0, num_criteria):
            user1_criterion_item_ratings =\
                extractor.get_matrix_column(user1_multi_ratings, i)
            user2_criterion_item_ratings =\
                extractor.get_matrix_column(user2_multi_ratings, i)

            total_similarity += similarity_calculator.calculate_similarity(
                user1_criterion_item_ratings, user2_criterion_item_ratings,
                self._similarity_metric)

        # We also add the overall similarity
        total_similarity += similarity_calculator.calculate_similarity(
            user1_overall_ratings, user2_overall_ratings,
            self._similarity_metric)

        average_similarity = total_similarity / (num_criteria + 1)

        return average_similarity
    def calculate_users_similarity(self, user_dictionary, user1, user2):

        common_items = extractor.get_common_items(user_dictionary, user1, user2)

        if not common_items:
            return None

        if self._min_common_items is not None and len(
                common_items) < self._min_common_items:
            return None

        user1_overall_ratings =\
            extractor.get_user_ratings(user_dictionary, user1, common_items)
        user1_multi_ratings =\
            extractor.get_user_multi_ratings(user_dictionary, user1, common_items)

        user2_overall_ratings =\
            extractor.get_user_ratings(user_dictionary, user2, common_items)
        user2_multi_ratings =\
            extractor.get_user_multi_ratings(user_dictionary, user2, common_items)

        num_criteria = len(user1_multi_ratings[0])
        total_similarity = 0.

        for i in xrange(0, num_criteria):
            user1_criterion_item_ratings =\
                extractor.get_matrix_column(user1_multi_ratings, i)
            user2_criterion_item_ratings =\
                extractor.get_matrix_column(user2_multi_ratings, i)

            total_similarity += similarity_calculator.calculate_similarity(
                user1_criterion_item_ratings, user2_criterion_item_ratings,
                self._similarity_metric)

        # We also add the overall similarity
        total_similarity += similarity_calculator.calculate_similarity(
            user1_overall_ratings, user2_overall_ratings, self._similarity_metric)

        average_similarity = total_similarity / (num_criteria + 1)

        return average_similarity
    def calculate_users_similarity(self, user_dictionary, user_id1, user_id2):
        """
        Calculates the similarity between two users based on how similar are
        their ratings in the reviews

        :param user_id1: the ID of user 1
        :param user_id2: the ID of user 2
        :return: a float with the similarity between the two users. Since this
        function is based on euclidean distance to calculate the similarity, a
        similarity of 0 indicates that the users share exactly the same tastes
        """
        user_weights1 = user_dictionary[user_id1].criteria_weights
        user_weights2 = user_dictionary[user_id2].criteria_weights

        return similarity_calculator.calculate_similarity(
            user_weights1, user_weights2, self._similarity_metric)
Example #6
0
    def calculate_users_similarity(self, user_dictionary, user1, user2):
        common_items = extractor.get_common_items(user_dictionary, user1, user2)

        if not common_items:
            return None

        if self._min_common_items is not None and len(
                common_items) < self._min_common_items:
            return None

        user1_ratings =\
            extractor.get_user_ratings(user_dictionary, user1, common_items)
        user2_ratings =\
            extractor.get_user_ratings(user_dictionary, user2, common_items)

        similarity_value = similarity_calculator.calculate_similarity(
            user1_ratings, user2_ratings, self._similarity_metric)

        return similarity_value