recommenders-team/recommenders

[ASK] Sequential Recommender (SLi_Rec) Questions

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#1,040 opened on Jan 22, 2020

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Description

Description

When we create a dataset for the predict function in SLi_Rec do we need to start with a label i.e. 1 or 0 and what timestamp should we use? If we use a future timestamp does the model provide a probability based on the timestamp? How about if we use the timestamp for NOW() or a timestamp in the past?

What are the tradeoffs/benefits for min_sequence_len and max_sequence_len in terms of network complexity and compute memory and time?

Is there a way to influence the alpha parameter that balances short-term and long-term?

What were the RAM requirements for the full Amazon dataset and the training data file size?

Any tips on providing the model with a more complex representation for Category. I see that under the hood there is an embedding happening, wondering if there is a way to include a combination of columns.

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Contributor guide

Research direction
Review the SLi Rec documentation and source code to understand the expected input format for predict, then experiment with different timestamp values and sequence length parameters. Check if alpha parameter is exposed. For RAM requirements, refer to the paper or train on a subset. For category representation, explore feature engineering with embeddings.
Tech stack
python
Domain
backendmachine learning
Issue type
Research
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.
Half day
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Needs maintainer response
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
PythonMachine Learning basics
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
45