Covers: theory of Sequence Based Recommendations- No User History
Estimated time needed to finish: 120 minutes
Questions this item addresses:
  • How can bi-directionality help with next item recommendations
How to use this item?

This is a foundational paper in this field so its highly recommended to read it all

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Sequence Based Recommendations

Contributors
Total time needed: ~6 hours
Objectives
New era recommendations engine utilizes the sequences of data rather than just the user history. The sequence based recommender engine utilizes models from NLP to analyze user behavior within a session
Potential Use Cases
Retail Users Behavior, Next best item
Who is This For ?
INTERMEDIATE
Click on each of the following annotated items to see details.
Resources5/6
ARTICLE 1. Sequence Analysis in Recommender Engine
  • How does Amazon, Netflix, YouTube finds the next best item for a given user
3 minutes
ARTICLE 2. Introduction to Sequence Modeling Problems
  • Different types of Seqeuence Modeeling and the differences among them?
6 minutes
PAPER 3. Recurrent Neural Networks with Top-k Gains for Session-basedRecommendations
  • How rNNwith top-k gains can help in ranking problem
2 hours
PAPER 4. BERT4REC
  • How can bi-directionality help with next item recommendations
2 hours
LIBRARY 5. BERT4REC code
  • A foundational paper that helps to understand how BERT can be used in recommendations problems
60 minutes
PAPER 6. SessNet
  • How to combine the user history and the sequence modelling into one network
60 minutes

Concepts Covered

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