PySpark Time Series Pipelines
Today's post covers the following:
- Basic pipeline conversion of timestamp to unix time
- Lag feature combination pipelines
- Aggregation based statistics pipelines
Today's post covers the following:
Today's post covers the following:
Todays post covers the following:
Todays post covers the following:
Todays post covers the following:
Todays post covers the following:
In this notebook we will look at how to use a neural network approach to making recommendations
user_id
and item_id
embeddings will be our relevancy scorespositive
feedback & negative samples which will be created randomly are our negative
samplestrain
will be used to train a model on historical user data, test
will be used to provide user recommendationsDSSM
in a previous notebook , well be simplifying things a little here, not including user and item features and will keep things more simple.In this post we'll cover some of the basics of recommendation system approaches utilising neural networks.
Collaborative filtering
(CF) is a recommendation generation method that relies on user-item interactions to make predictions about what a user might like based on the historical data of user interaction with the item.
We covered collaborative filtration
using matrix factorisation is the notebook.
Specifically we looked at methods such as SVD
in order to generate the user
and item
matrices, these two matrices are then multiplied together to get the corresponding scores for each user, item pairing; a model-based approach.
Neural Collaborative Filtering
(NCF) bears some similarity to CF whilst leveraging the benefits of deep learning techniques to enhance recommendation performance.
Uplift modeling is a predictive modeling technique that aims to identify the individuals who are most likely to respond positively to a specific treatment or intervention. This technique is particularly useful in marketing and customer relationship management, where the goal is to target customers who are likely to be influenced by a marketing campaign or offer. By distinguishing between those who are positively influenced by the treatment and those who are not, uplift modeling helps organizations optimize their targeting strategies and maximize the return on investment of their marketing efforts.
An interview question related to SQL knowledge from a financial bank which I thought was interesting so decided to share
python
and posgres