Add recommend API in Recommender

cornac/models/recommender.py

@@ -292,6 +292,53 @@ def rank(self, user_idx, item_indices=None):
 
         return item_rank, item_scores
 
+    def recommend(self, user_id, k=-1, remove_seen=False):
+        """Generate top-K item recommendations for a given user. Key difference between
+        this function and rank() function is that rank() function works with mapped
+        user/item index while this function works with original user/item ID. This helps
+        hide the abstraction of ID-index mapping, and make model usage and deployment cleaner.
+
+        Parameters
+        ----------
+        user_id: str, required
+            The original ID of the user.
+
+        k: int, optional, default=-1
+            Cut-off length for recommendations, k=-1 will return ranked list of all items.
+
+        remove_seen: bool, optional, default: False
+            Remove seen/known items during training and validation from output recommendations.
+
+        Returns
+        -------
+        output: list
+            Recommended items in the form of their original IDs.
+        """
+        user_idx = self.train_set.uid_map.get(user_id, -1)
+        if user_idx == -1:
+            raise ValueError(f"{user_id} is unknown to the model.")
+
+        if k < -1 or k > self.train_set.total_items:
+            raise ValueError(
+                f"k={k} is invalid, there are {self.train_set.total_users} users in total."
+            )
+
+        item_indices = np.arange(self.train_set.total_items)
+        if remove_seen:
+            unk_mask = np.ones(len(item_indices), dtype="bool")
+            if not self.train_set.is_unk_user(user_idx):
+                unk_mask[self.train_set.csr_matrix.getrow(user_idx).indices] = False
+            if not self.val_set is None and not self.val_set.is_unk_user(user_idx):
+                unk_mask[self.val_set.csr_matrix.getrow(user_idx).indices] = False
+            item_indices = item_indices[unk_mask]
+
+        item_rank, _ = self.rank(user_idx, item_indices)
+        if k != -1:
+            item_rank = item_rank[:k]
+        output = [self.train_set.item_ids[i] for i in item_rank]
+
+        return output
+
     def monitor_value(self):
         """Calculating monitored value used for early stopping on validation set (`val_set`).
         This function will be called by `early_stop()` function.