nihalsangeeth/behaviour-seq-transformer

Pytorch implementation of "Behaviour Sequence Transformer for E-commerce Recommendation" as a seq2seq predictor.

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This helps e-commerce platforms predict the next item a customer is likely to interact with based on their past behavior sequence. You feed in a customer's history of viewed or purchased items, and it outputs a prediction of what they might engage with next. This is for product managers, merchandisers, or data scientists working on personalization and recommendation systems in online retail.

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Use this if you need to build a recommendation engine that suggests the very next item a user will engage with, rather than just predicting a click-through rate.

Not ideal if your primary goal is to predict the likelihood of a user clicking on a specific item (Click-Through Rate prediction) rather than predicting the actual next item in a sequence.

e-commerce product-recommendation customer-behavior personalization retail-analytics
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 17 / 25

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Stars

23

Forks

9

Language

Python

License

MIT

Last pushed

Aug 25, 2021

Commits (30d)

0

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