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Annotation 7777916161292

#RNN #ariadne #behaviour #consumer #deep-learning #patterns #recurrent-neural-networks #retail #simulation #synthetic-data
Deep learning techniques can assist marketing decision makers to provide more reliable and practical marketing strategic plans. In this paper, we propose a customer behaviour prediction model using recurrent neural networks (RNNs) based on the client loyalty number (CLN), recency, frequency, and monetary (RFM) variables. The experiment results show that RNNs can predict RFM values of customers efficiently. This model can be later used in recommender systems for exclusive promotional offers and loyalty programs management.
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omputing systems have provided a great opportunity to model and predict customer behaviour. However, there is a lack of modern modelling and analytical methods to perform analysis on such data. <span>Deep learning techniques can assist marketing decision makers to provide more reliable and practical marketing strategic plans. In this paper, we propose a customer behaviour prediction model using recurrent neural networks (RNNs) based on the client loyalty number (CLN), recency, frequency, and monetary (RFM) variables. The experiment results show that RNNs can predict RFM values of customers efficiently. This model can be later used in recommender systems for exclusive promotional offers and loyalty programs management. <span>

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Flashcard 7777917734156

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
For session-stream RNNs, history inputs st ∈ R23 represent sessions with binary indicators which [...] occurred, the time difference to the previous session and the characteristics described in Sec. 3.2.
Answer
action types

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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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Annotation 7777920093452

#abm #agent-based #priority #rooftop-solar #simulation #synthetic-data
Agent-based modeling (ABM) has long been a common framework of choice for studying aggregate, or emergent, properties of complex systems as they arise from microbehaviors of a multitude of agents in social and economic contexts
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Agent-based modeling (ABM) has long been a common framework of choice for studying aggregate, or emergent, properties of complex systems as they arise from microbehaviors of a multitude of agents in social and economic contexts [7,30,35]. ABM appears well-suited to policy experimentation of just the kind needed for the rooftop solar market. Indeed, there have been several attempts to develop agent-based models

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