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on 19-Jan-2023 (Thu)

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

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.

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#feature-engineering #lstm #recurrent-neural-networks #rnn
All four customers in the figure have the same seniority (date of first purchase), recency (date of last purchase), and frequency (number of purchases). However, each of them has a visibly different transaction pattern. A response model relying exclusively on seniority, recency, and frequency would not be able to distinguish between customers who have similar featu res but different behavioral sequence.
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#deep-learning #keras #lstm #python #sequence

7.1 The Stacked LSTM

The Stacked LSTM is a model that has multiple hidden LSTM layers where each layer contains multiple memory cells. We will refer to it as a Stacked LSTM here to differentiate it from the unstacked LSTM (Vanilla LSTM) and a variety of other extensions to the basic LSTM model.

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Flashcard 7559874743564

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#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
All models are trained to [...] negative log-likelihood (NLL).
Answer
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All models are trained to minimize negative log-likelihood (NLL).

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#deep-learning #keras #lstm #python #sequence
How to Convert Categorical Data to Numerical Data This involves two steps: 1. Integer Encoding. 2. One Hot Encoding.
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3.2.1 How to Convert Categorical Data to Numerical Data This involves two steps: 1. Integer Encoding. 2. One Hot Encoding. Integer Encoding As a first step, each unique category value is assigned an integer value. For example, red is 1, green is 2, and blue is 3. This is called label encoding or an integer

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#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
feature engineering: a fixed set of identifiers f i has to be designed to capture the essence of an individual consumer history. Only signals that are encoded in the feature vector can be picked up by the prediction model. Defining expressive features often requires both, domain knowledge as well as data-science intuition
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ed machine learning methods like logistic regression take vectors f = (f 1 , . . . , f n ) of fixed length n as inputs. Applying these methods on consumer histories of arbitrary length requires <span>feature engineering: a fixed set of identifiers f i has to be designed to capture the essence of an individual consumer history. Only signals that are encoded in the feature vector can be picked up by the prediction model. Defining expressive features often requires both, domain knowledge as well as data-science intuition. <span>

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#deep-learning #keras #lstm #python #sequence
The internal state in LSTM layers is also accumulated when evaluating a network and when making predictions
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The internal state in LSTM layers is also accumulated when evaluating a network and when making predictions. Therefore, if you are using a stateful LSTM, you must reset state after evaluating the network on a validation dataset or after making predictions

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#deep-learning #keras #lstm #python #sequence
The Stacked LSTM is a model that has multiple hidden LSTM layers where each layer contains multiple memory cells.
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7.1 The Stacked LSTM The Stacked LSTM is a model that has multiple hidden LSTM layers where each layer contains multiple memory cells. We will refer to it as a Stacked LSTM here to differentiate it from the unstacked LSTM (Vanilla LSTM) and a variety of other extensions to the basic LSTM model.

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#feature-engineering #lstm #recurrent-neural-networks #rnn
In machine learning, a feature refers to a variable that describes some aspect of individual data objects (Dong & Liu, 2018).
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In machine learning, a feature refers to a variable that describes some aspect of individual data objects (Dong & Liu, 2018). Feature engineering has been used broadly to refer to multiple aspects of feature creation, extraction, and transformation. Essentially, it refers to the process of using domain knowled

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