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Annotation 7096399432972

#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
RNNs operate on sequences of varying lengths and therefore provide an appropriate match to consumer histories.
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Annotation 7100409974028

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

Learning Machine Architecture

The proposed RNN model is consisted of one input layer, one hidden (recurrent) layer, and one output layer. The input layer is an auto-encoder which extracts features from inputs. The CLN, R, F, and M values for each customer at each time- step t are the input sequence to the RNN model presented in Figure 4. The R, F, and M value of the next time-step t + 1 is shown to the model as target through predefined time intervals. The time-step t can be set depending on the application. For example, for grocery stores it can be weekly or bi-weekly and for sports wear every season
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Flashcard 7104191663372

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
RNNs operate on sequences of [...] lengths and therefore provide an appropriate match to consumer histories.
Answer
varying

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RNNs operate on sequences of varying lengths and therefore provide an appropriate match to consumer histories.

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Flashcard 7104193498380

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
For event-stream RNNs, history inputs x t ∈ R 20 consist of a one-hot encoding of the action type and the [...]. For session-stream RNNs, history inputs s t ∈ R 23 represent sessions with binary indicators which action types occurred, the time difference to the previous session and the characteristics described in Sec. 3.2. Time differences and, in case of session-stream RNNs, the total session event counts are logarithmized.
Answer
time difference

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For event-stream RNNs, history inputs x t ∈ R 20 consist of a one-hot encoding of the action type and the time difference. For session-stream RNNs, history inputs s t ∈ R 23 represent sessions with binary indicators which action types occurred, the time difference to the previous session and the characteri

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Flashcard 7104195333388

Question

Predictive customer scores

The company develops analytics—often using several types of machine-learning algorithms—to understand and track what is influencing customer [...] and business performance, and to detect specific events in customer journeys.

Answer
satisfaction

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Predictive customer scores The company develops analytics—often using several types of machine-learning algorithms—to understand and track what is influencing customer satisfaction and business performance, and to detect specific events in customer journeys.

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Flashcard 7104197692684

Tags
#causality #statistics
Question
In causal graphs, [...] flows along directed paths.
Answer
causation

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In causal graphs, causation flows along directed paths.

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Annotation 7104206605580

时间管理的技巧
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AhaClub X 少楠：“我想用flomo改善一代人的思考方式”（附文字版）_产品沉思录-商业新知
以领到AhaFM专属小红包哦~~ 访谈实录： Aha X 少楠 （本文将近2万字，整理by mark，编辑by ivy，读不完的童鞋建议选择更轻松的播客形式收听~） 目录： 1/ 个人与时代：产品经理的职业变迁 2/ PM的核心能力项 ● 好奇心驱使 ● 别拍脑袋，去现场 ● 用抽象能力精简架构 ● 做减法而不是做加法 3/ 其他建议 ● 在忙乱的互联网行业寻找自己、注重底层思考 ● <span>时间管理的技巧：复用 4/ flomo缘何而来？ ● 私人数据库＋积累与拼装＋去收藏化＋思维锻炼 5/ flomo怎么做增长？ 6/ “老家伙” 对职场新人的留言 个人与时代： 产品经理的职业变迁 我在产品经理这个岗位上已经干了十几年了，经历了整个所谓的古典互联网到产业互联网的历史。 我本人大概经历是，第一段是在周末画报，当时做i-weekly 的项目，这也是比较老了，当时我们是第一