public code v1

pygrex/explain/individual/model_based_als_explain.py

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+import numpy as np
+import pandas as pd
+
+from .explainer import Explainer
+
+
+class ALSExplainer(Explainer):
+    def __init__(self, model, recommendations, data, number_of_contributions=10):
+        super(ALSExplainer, self).__init__(model, recommendations, data)
+        self.number_of_contributions = number_of_contributions
+
+    def explain_recommendation_to_user(self, user_id: int, item_id: int):
+        """
+        Measuring the contribution of each item to the recommendation.
+        :param model:
+        :param item_id:
+        :param user_id:
+        :return: returns a dataframe with the contribution to the recommendation of each previously interacted with item.
+        """
+
+        current_interactions = np.zeros(self.num_items)
+        current_interactions[self.get_user_items(user_id)] = 1
+
+        c_u = np.diag(current_interactions)
+
+        y_t = self.model.item_embedding().transpose()
+        temp = np.matmul(y_t, c_u)
+        temp = np.matmul(temp, self.model.item_embedding())
+        temp = temp + np.diag([self.model.reg_term] * self.model.latent_dim)
+
+        if len(self.get_user_items(user_id)) > 1:
+            weight_mtr = np.linalg.inv(temp)
+        else:
+            weight_mtr = np.linalg.pinv(temp)
+
+        temp = np.matmul(self.model.item_embedding(), weight_mtr)
+
+        sim_to_rec_id = temp.dot(self.model.item_embedding()[item_id, :])
+
+        sim_to_rec_id = sim_to_rec_id[self.get_user_items(user_id)]
+
+        contribution = {
+            "item": self.get_user_items(user_id),
+            "contribution": sim_to_rec_id,
+        }
+        contribution = pd.DataFrame(contribution)
+        contribution = contribution.sort_values(by=["contribution"], ascending=False)
+        return {
+            "item": contribution.item[: self.number_of_contributions],
+            "contribution": contribution.contribution[: self.number_of_contributions],
+        }