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on 01-Apr-2025 (Tue)

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Annotation 7693164481804

#recurrent-neural-networks #rnn
We demonstrate the model performance in eight empirical real-life settings which vary broadly in transaction frequency, purchase (ir)regularity, customer attrition, availability of contextual information, seasonal variance, and cohort size. We showcase the flexibility of the approach and how the model further benefits from taking into account static (e.g., socio-economic variables, demographics) and dynamic context factors (e.g., weather, holiday seasons, marketing appeals)
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. It also helps managers in capturing seasonal trends and other forms of purchase dynamics that are important to detect in a timely manner for the purpose of proactive customer-base management. <span>We demonstrate the model performance in eight empirical real-life settings which vary broadly in transaction frequency, purchase (ir)regularity, customer attrition, availability of contextual information, seasonal variance, and cohort size. We showcase the flexibility of the approach and how the model further benefits from taking into account static (e.g., socio-economic variables, demographics) and dynamic context factors (e.g., weather, holiday seasons, marketing appeals) <span>

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Annotation 7693166054668

#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
We use a simple RNN architecture with a single LSTM layer and ten-dimensional cell states. The hidden state at the last time-step is combined with binary non-history features to make the final prediction in a logistic layer. Thus, the final prediction of the RNN is linear in the learned and non-history features. The non-history features describe time, weekday, and behavioral gender and are also provided to the baseline methods
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RNN details We use a simple RNN architecture with a single LSTM layer and ten-dimensional cell states. The hidden state at the last time-step is combined with binary non-history features to make the final prediction in a logistic layer. Thus, the final prediction of the RNN is linear in the learned and non-history features. The non-history features describe time, weekday, and behavioral gender and are also provided to the baseline methods. Instead of absolute timestamps, the time differences ∆(x t−1 , x t ) to the previous inputs x t−1 are fed to the RNN at each time- step t. Furthermore, the difference between the last

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Flashcard 7693167889676

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
Instead of absolute timestamps, the time [...] ∆(x t−1 , x t ) to the previous inputs x t−1 are fed to the RNN at each time- step t.
Answer
differences

statusnot learnedmeasured difficulty37% [default]last interval [days]               
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scheduled repetition interval               last repetition or drill

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Instead of absolute timestamps, the time differences ∆(x t−1 , x t ) to the previous inputs x t−1 are fed to the RNN at each time- step t.

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