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Add RP3Beta #634

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Add RP3Beta #634

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deklanw
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@deklanw deklanw commented Dec 29, 2020

Random walk method from Updatable, Accurate, Diverse, and Scalable Recommendations for Interactive Applications https://dl.acm.org/doi/10.1145/2955101

specific_user_transitions = self.user_transition[user]

# make all item predictions for specified users
user_all_items = specific_user_transitions @ self.item_transition @ self.user_transition @ self.item_degree_inv.power(
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It seems that the whole user_all_items matrix can be computed in advance to avoid multiplication in each batch.

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@deklanw deklanw Mar 15, 2022

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@linzihan-backforward Hello, sorry for the (very) late reply. I intentionally avoided computing the entire user_item matrix to save memory. I included the all-at-once implementation at the top for reference, and explained the former. See

# for reference, doing it in one computation
# since the resultant matrix is dense, I'll refrain from doing this
def calculate_rp3beta(B, beta):
user_degree_inv = get_inv_degree_matrix(B)
item_degree_inv = get_inv_degree_matrix(B.T)
# multiplication on left for row-wise scaling
user_transition = user_degree_inv @ B
item_transition = item_degree_inv @ B.T
P3 = user_transition @ item_transition @ user_transition
# multiplication on right for column-wise scaling (i.e., we're reweighting by inverse item popularity to a power)
RP3Beta = P3 @ item_degree_inv.power(beta)
return RP3Beta

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It make sense. We will test the algorithm as soon as possible. It will be merged into the future version if everything is OK. Thanks again for your contribution !


specific_user_transitions = self.user_transition[user]

item_predictions = specific_user_transitions @ self.item_transition @ self.user_transition @ self.item_degree_inv.power(
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Same as above.

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