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Question about Wide and Deep Model for Movie Recommendation

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#1,206 opened on Sep 23, 2020

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Description

Description

in this example there are some itemID(movie_id) in test data that they don't exists in train data, so during training the model can't build or it is better to say "can't update" embedding for them. in normal situation it's not a problem but "ranking_pool" that was used for evaluation also contains these itemIDs pair with some userIDs therefore they have negative affects on evaluation metric like NDCG because we multiplied some random vectors with learned user vectors. Is there any reason for doing so?

Contributor guide

Research direction
Investigate the data splitting method used in the quick start notebook, and examine how the ranking pool for evaluation is constructed. Discuss whether including unseen items in the test set is intentional and how it affects NDCG.
Tech stack
python
Domain
aibackend
Issue type
Bug
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
PythonMachine Learning
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
65