Protocf: Prototypical collaborative filtering for few-shot recommendation
In recent times, deep learning methods have supplanted conventional collaborative filtering
approaches as the backbone of modern recommender systems. However, their gains are …
approaches as the backbone of modern recommender systems. However, their gains are …
Next-item recommendations in short sessions
The changing preferences of users towards items trigger the emergence of session-based
recommender systems (SBRSs), which aim to model the dynamic preferences of users for …
recommender systems (SBRSs), which aim to model the dynamic preferences of users for …
Sequential scenario-specific meta learner for online recommendation
Cold-start problems are long-standing challenges for practical recommendations. Most
existing recommendation algorithms rely on extensive observed data and are brittle to …
existing recommendation algorithms rely on extensive observed data and are brittle to …
CAML: Contextual augmented meta-learning for cold-start recommendation
The performance of recommendation engines is challenged by the cold-start issues.
Classical recommendation techniques have limited capability to address this problem …
Classical recommendation techniques have limited capability to address this problem …
Towards open-world recommendation: An inductive model-based collaborative filtering approach
Recommendation models can effectively estimate underlying user interests and predict
one's future behaviors by factorizing an observed user-item rating matrix into products of two …
one's future behaviors by factorizing an observed user-item rating matrix into products of two …
Towards flexible and adaptive neural process for cold-start recommendation
Recommender systems have been widely adopted in various online personal e-commerce
applications for improving user experience. A long-standing challenge in recommender …
applications for improving user experience. A long-standing challenge in recommender …
How to retrain recommender system? A sequential meta-learning method
Practical recommender systems need be periodically retrained to refresh the model with
new interaction data. To pursue high model fidelity, it is usually desirable to retrain the …
new interaction data. To pursue high model fidelity, it is usually desirable to retrain the …
Pre-trained recommender systems: A causal debiasing perspective
Recent studies on pre-trained vision/language models have demonstrated the practical
benefit of a new, promising solution-building paradigm in AI where models can be pre …
benefit of a new, promising solution-building paradigm in AI where models can be pre …
Memory-augmented meta-learning framework for session-based target behavior recommendation
B Yu, X Li, J Fang, C Tai, W Cheng, J Xu - World Wide Web, 2023 - Springer
Session-based recommendation aims to predict the next item to be interacted by a specific
type of behavior (eg, click or purchase) within a session. However, the main challenge …
type of behavior (eg, click or purchase) within a session. However, the main challenge …
M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations
Session-based recommender systems (SBRSs) have shown superior performance over
conventional methods. However, they show limited scalability on large-scale industrial …
conventional methods. However, they show limited scalability on large-scale industrial …