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A response model relying exclusively on seniority, recency, and frequency would not be able to distinguish between customers who have similar features but different behavioral sequence

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We can summarize the 3 key benefits of LSTMs as: 3. Process input sequences and output sequences time step by time step, allowing variable length inputs and outputs

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Flashcard 7674348309772

Tags

#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data

Question

For session-stream RNNs, history inputs s_t ∈ R²³ represent sessions with [...] indicators which action types occurred, the time difference to the previous session and the characteristics described in Sec. 3.2.

Answer

binary

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

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For session-stream RNNs, history inputs st ∈ R23 represent sessions with binary indicators which action types occurred, the time difference to the previous session and the characteristics described in Sec. 3.2.

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on 10-Jan-2025 (Fri)

Flashcard 7674344377612

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Flashcard 7674346474764

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Flashcard 7674348309772

Parent (intermediate) annotation

Original toplevel document (pdf)