@@ -55,7 +55,7 @@ def average_precision_at_k(relevant_items: np.array, recommendation: np.array, k
5555 Returns:
5656 AP@K (float): The average precision @ k of a predicted list.
5757
58- `Original: <https://github.com/benhamner/Metrics/blob/master/Python/ml_metrics/average_precision.py>`_
58+ `Original <https://github.com/benhamner/Metrics/blob/master/Python/ml_metrics/average_precision.py>`_
5959 """
6060
6161 if len (recommendation ) > k :
@@ -228,7 +228,7 @@ def intra_list_similarity(recommendations: List[list], items_feature_matrix: np.
228228 Returns:
229229 (float): Average intra list similarity across predicted
230230
231- `Original: <https://github.com/statisticianinstilettos/recmetrics/blob/master/recmetrics/metrics.py#L232>`_
231+ `Original <https://github.com/statisticianinstilettos/recmetrics/blob/master/recmetrics/metrics.py#L232>`_
232232 """
233233
234234 intra_list_similarities = []
@@ -254,7 +254,7 @@ def personalization(recommendations: List[list]):
254254 Returns:
255255 (float): personalization
256256
257- `Original: <https://github.com/statisticianinstilettos/recmetrics/blob/master/recmetrics/metrics.py#L160>`_
257+ `Original <https://github.com/statisticianinstilettos/recmetrics/blob/master/recmetrics/metrics.py#L160>`_
258258 """
259259
260260 n_predictions = len (recommendations )
@@ -306,7 +306,7 @@ def novelty(recommendations: List[list], item_popularities: dict, num_users: int
306306 Solving the apparent diversity-accuracy dilemma of recommender systems.
307307 Proceedings of the National Academy of Sciences, 107(10), 4511-4515.
308308
309- `Original: <https://github.com/statisticianinstilettos/recmetrics/blob/master/recmetrics/metrics.py#L14>`_
309+ `Original <https://github.com/statisticianinstilettos/recmetrics/blob/master/recmetrics/metrics.py#L14>`_
310310 """
311311
312312 epsilon = 1e-10
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