Edited, memorised or added to reading queue

on 24-Jan-2023 (Tue)

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

Annotation 7104219974924

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

This paper proposes a new model for RFM prediction of customers based on recurrent neural networks (RNNs) with rectified linear unit activation function. The model utilizes an auto-encoder to represent features of input parameters (i.e. customer loyalty number, R, F, and M).

Customer Shopping Pattern Prediction- A Recurrent Neural Network Approach.pdf, p6
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
This paper proposes a new model for RFM prediction of customers based on recurrent neural networks (RNNs) with rectified linear unit activation function. The model utilizes an auto-encoder to represent features of input parameters (i.e. customer loyalty number, R, F, and M). The proposed model is the first of its kind in the literature and has many opportunities for further improvement. The model can be improved by using more training data. It is interestin

Original toplevel document (pdf)

cannot see any pdfs



Flashcard 7560161791244

Tags
#English #vocabulary
Question

eschew

/ɪsˈtʃuː,ɛsˈtʃuː/

Learn to pronounce

verb

gerund or present participle: eschewing

  1. deliberately [...] using; abstain from.

    "he appealed to the crowd to eschew violence"

Answer
avoid

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

Open it
eschew /ɪsˈtʃuː,ɛsˈtʃuː/ Learn to pronounce verb gerund or present participle: eschewing deliberately avoid using; abstain from. "he appealed to the crowd to eschew violence"






Annotation 7560163626252

#deep-learning #embeddings
With the similar idea of how we get word embeddings, we can make an analogy like this: a word is like a product; a sentence is like a sequence of ONE customer’s shopping sequence; an article is like a sequence of ALL customers’ shopping sequence
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
With the similar idea of how we get word embeddings, we can make an analogy like this: a word is like a product; a sentence is like a sequence of ONE customer’s shopping sequence; an article is like a sequence of ALL customers’ shopping sequence. This embedding technique allows us to represent product or user as low dimensional continuous vectors, while the one-hot encoding method will lead to the curse of dimensionality for th

Original toplevel document (pdf)

cannot see any pdfs



Annotation 7560165985548

#deep-learning #keras #lstm #python #sequence
Careful choice must be given to the number of time steps specified when preparing your input data for sequence prediction problems in Keras
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
Careful choice must be given to the number of time steps specified when preparing your input data for sequence prediction problems in Keras. The choice of time steps will influence both: - The internal state accumulated during the forward pass. - The gradient estimate used to update weights on the backward pass. Note that b

Original toplevel document (pdf)

cannot see any pdfs



Annotation 7560167820556

#recurrent-neural-networks #rnn
Regression-type models and traditional ML methods are often criticized for their backward-looking properties and inefficient use of the available data (because they need to hold out the most recent period of transaction histories to construct the dependent variable; cf. Fader & Hardie (2009)). This limitation implies the inability to make projections into the distant future, but despite the ‘‘one time step ahead” property of its predictions we show, by means of a calibration length sensitivity study, that the proposed approach can leverage the complete transaction histories and deliver excellent long-term forecasts for individual customers.
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
iates is in principle possible with so-called ‘‘scoring” or regression-like models and, to a certain extent, with advanced probability models as well, our approach comes with another advantage. <span>Regression-type models and traditional ML methods are often criticized for their backward-looking properties and inefficient use of the available data (because they need to hold out the most recent period of transaction histories to construct the dependent variable; cf. Fader & Hardie (2009)). This limitation implies the inability to make projections into the distant future, but despite the ‘‘one time step ahead” property of its predictions we show, by means of a calibration length sensitivity study, that the proposed approach can leverage the complete transaction histories and deliver excellent long-term forecasts for individual customers. Such a perspective seems to be particularly useful for the rich stream of information accompanying customer-firm interactions in modern digital business environments (Wedel & Kannan

Original toplevel document (pdf)

cannot see any pdfs



Flashcard 7560171490572

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

This paper proposes a new model for RFM prediction of customers based on recurrent neural networks (RNNs) with rectified linear unit activation function. The model utilizes an [...] to represent features of input parameters (i.e. customer loyalty number, R, F, and M).

Customer Shopping Pattern Prediction- A Recurrent Neural Network Approach.pdf, p6

Answer
auto-encoder

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
This paper proposes a new model for RFM prediction of customers based on recurrent neural networks (RNNs) with rectified linear unit activation function. The model utilizes an auto-encoder to represent features of input parameters (i.e. customer loyalty number, R, F, and M). Customer Shopping Pattern Prediction- A Recurrent Neural Network Approach.pdf, p6

Original toplevel document (pdf)

cannot see any pdfs