Update Hybrid algo classification to align with Recommenders book and Aggarwal #2050
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Signed-off-by: miguelgfierro <miguelgfierro@users.noreply.github.com>
Signed-off-by: miguelgfierro <miguelgfierro@users.noreply.github.com>
Signed-off-by: miguelgfierro <miguelgfierro@users.noreply.github.com>
Signed-off-by: miguelgfierro <miguelgfierro@users.noreply.github.com>
miguelgfierro
requested review from
gramhagen,
anargyri,
loomlike,
wutaomsft and
SimonYansenZhao
as code owners
SimonYansenZhao
approved these changes
Jan 8, 2024
miguelgfierro
commented
Jan 8, 2024
| @@ -83,12 +83,12 @@ The table below lists the recommender algorithms currently available in the repo | |||
| | Cornac/Bilateral Variational Autoencoder (BiVAE) | Collaborative Filtering | Generative model for dyadic data (e.g., user-item interactions). It works in the CPU/GPU environment. | [Deep dive](examples/02_model_collaborative_filtering/cornac_bivae_deep_dive.ipynb) | | |||
| | Convolutional Sequence Embedding Recommendation (Caser) | Collaborative Filtering | Algorithm based on convolutions that aim to capture both user’s general preferences and sequential patterns. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) | | |||
| | Deep Knowledge-Aware Network (DKN)<sup>*</sup> | Content-Based Filtering | Deep learning algorithm incorporating a knowledge graph and article embeddings for providing news or article recommendations. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/dkn_MIND.ipynb) / [Deep dive](examples/02_model_content_based_filtering/dkn_deep_dive.ipynb) | | |||
| | Extreme Deep Factorization Machine (xDeepFM)<sup>*</sup> | Hybrid | Deep learning based algorithm for implicit and explicit feedback with user/item features. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/xdeepfm_criteo.ipynb) | | |||
| | Extreme Deep Factorization Machine (xDeepFM)<sup>*</sup> | Collaborative Filtering | Deep learning based algorithm for implicit and explicit feedback with user/item features. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/xdeepfm_criteo.ipynb) | | |||
There was a problem hiding this comment.
@anargyri what do you think of the change?
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Description
Recommenders book ( from Tao, Le and Simon, Xing, etc) classifies FM as a collaborative filtering algo. Also, in the Aggarwal book, the hybrid reco is a combination of CF and CBF.
This PR updates the algo types to align with these books.
Related Issues
References
Checklist:
staging branchand not tomain branch.