Update Hybrid algo classification to align with Recommenders book and Aggarwal #2050
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Signed-off-by: miguelgfierro <[email protected]>
Signed-off-by: miguelgfierro <[email protected]>
Signed-off-by: miguelgfierro <[email protected]>
Signed-off-by: miguelgfierro <[email protected]>
Signed-off-by: miguelgfierro <[email protected]>
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) | | |||
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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.