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May 7, 2023 · We propose a Contextually Augmented Meta-Learning recommender system (CAML). The proposed method augments the contextual features into a meta-learning model.
We propose a Contextually Augmented Meta-Learning recommender system (CAML). The proposed method augments the contextual features into a meta-learning model.
May 7, 2023 · The augmented samples are then forwarded to a Meta-Learner (MetaL) to learn user preferences and generate relevant recommendations. Additionally ...
Nov 21, 2023 · The key idea of CAML is to introduce a data augmentation unit that uses a hybrid similarity to augment data samples from similar neighbors. ...
Aug 24, 2021 · We propose a recommendation framework called Contextual Modulation Meta Learning (CMML). CMML is composed of fully feed-forward operations so it is ...
Mar 1, 2023 · The proposed method augments the contextual features into a meta-learning model which considerably improves the tasks adaptation capability. We ...
2023. TLDR. This work proposes a preference learning decoupling framework, which is enhanced with meta-augmentation (PDMA), for user cold-start recommendation ...
Missing: CAML: | Show results with:CAML:
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Sequential recommendation systems in cold-start scenarios aim to provide recommendations as accurately as possible for users with sparse behavior, which is a ...
It consists of, a context encoder that can generate context embedding to represent a specific task, a hybrid context generator that aggregates specific user- ...
Missing: CAML: | Show results with:CAML:
Ur Rehman introduced a model termed Contextually. Augmented Meta-Learning (CAML), which enhances contextual information by forwarding user context to a Data.