Edited, memorised or added to reading queue

on 09-Jul-2022 (Sat)

Do you want BuboFlash to help you learning these things? Click here to log in or create user.

Annotation 7096393927948

#RNN #ariadne #behaviour #consumer #deep-learning #priority #retail #simulation #synthetic-data
In this paper, we present our study of consumer purchase behaviour, wherein, we establish a data-driven framework to predict whether a consumer is going to purchase an item within a certain time frame using e-commerce retail data. To model this relationship, we create a sequential time-series data for all relevant consumer-item combinations. We then build generalized non-linear models by generating features at the intersection of consumer, item, and time.
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

pdf

cannot see any pdfs



Annotation 7096397860108

#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
In this paper, we show that recurrent neural networks (RNNs) are promising to overcome both shortcomings of vector-based methods, tedious feature engineering and lack of explainability.
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

pdf

cannot see any pdfs



Annotation 7096404151564

#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
To summarize, our contributions are the following: (i) we show how consumer behavior can be predicted without sophisticated feature engineering by using RNNs; (ii) we provide an empirical comparison of prediction performance on real-world e-commerce data; and (iii) we demonstrate how RNNs are helpful in explaining the predictions for individual consumers
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

pdf

cannot see any pdfs



Annotation 7096420142348

#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
As probability estimates are required directly in many practical applications, we use NLL also for evaluation. In some applications, the resulting ranking of consumers is more important than the probabilities themselves. For this reason, we also report the area under the ROC curve (AUC)
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

pdf

cannot see any pdfs



Annotation 7103214914828

#abm #agent-based #machine-learning #model #priority
We showed that by using the presented framework it is possible to implement an agent- based model without the need to manually find rules or equations for agent behaviour, which is the most challenging step for most agent-based models. Within the framework, agents first make random decisions and gather experience. Then a Neural Network is trained to be able to judge a combination of (sensory) input and a decision, classifying this decision as positive, negative or neutral. Here, the Neural Network is not used as a form of optimization, but rather as a realistic depiction of a decision process, including the possibility of errors in judgement.
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
Thus, we expanded the framework to an iterative process, thereby increasing its scope to systems that cannot be explored well using random agent decisions. 4. Conclusion We showed that by using the presented framework it is possible to implement an agent- based model without the need to manually find rules or equations for agent behaviour, which is the most challenging step for most agent-based models. Within the framework, agents first make random decisions and gather experience. Then a Neural Network is trained to be able to judge a combination of (sensory) input and a decision, classifying this decision as positive, negative or neutral. Here, the Neural Network is not used as a form of optimization, but rather as a realistic depiction of a decision process, including the possibility of errors in judgement. We demonstrated the advantages of this approach by applying it to reproduce the results of the prominent Sugarscape model. To show the flexibility of the framework, we then made slight

Original toplevel document (pdf)

cannot see any pdfs



Flashcard 7103218846988

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
In this paper, we show that recurrent neural networks (RNNs) are promising to overcome both shortcomings of [...]-based methods, tedious feature engineering and lack of explainability.
Answer
vector

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
In this paper, we show that recurrent neural networks (RNNs) are promising to overcome both shortcomings of vector-based methods, tedious feature engineering and lack of explainability.

Original toplevel document (pdf)

cannot see any pdfs






Annotation 7103222517004

#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Consumer histories are inherently sequential and of varying lengths T , making RNNs a natural model choice.
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
Event-stream RNNs We propose to model the behavior of consumers with RNNs. Consumer histories are inherently sequential and of varying lengths T , making RNNs a natural model choice. In e-commerce, available data sources and prediction scenarios often change, making the generality of RNNs appealing as no problem-specific feature engineering has to take place.

Original toplevel document (pdf)

cannot see any pdfs



Flashcard 7103224089868

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
Event-stream RNNs We propose to model the behavior of consumers with RNNs. Consumer histories are inherently sequential and of varying lengths T , making RNNs a natural model choice. In e-commerce, available data sources and prediction scenarios often change, making the generality of RNNs appealing as no problem-specific [...] has to take place.
Answer
feature engineering

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
rying lengths T , making RNNs a natural model choice. In e-commerce, available data sources and prediction scenarios often change, making the generality of RNNs appealing as no problem-specific <span>feature engineering has to take place. <span>

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7103227759884

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
As probability estimates are required directly in many practical applications, we use NLL also for evaluation. In some applications, the resulting ranking of consumers is more important than the probabilities themselves. For this reason, we also report the [...]
Answer
area under the ROC curve (AUC)

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
l applications, we use NLL also for evaluation. In some applications, the resulting ranking of consumers is more important than the probabilities themselves. For this reason, we also report the <span>area under the ROC curve (AUC) <span>

Original toplevel document (pdf)

cannot see any pdfs