In recent times, deep learning methods have supplanted conventional collaborative filtering
approaches as the backbone of modern recommender systems. However, their gains are …

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 …

Cold-start problems are long-standing challenges for practical recommendations. Most
existing recommendation algorithms rely on extensive observed data and are brittle to …

The performance of recommendation engines is challenged by the cold-start issues.
Classical recommendation techniques have limited capability to address this problem …

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 …

Recommender systems have been widely adopted in various online personal e-commerce
applications for improving user experience. A long-standing challenge in recommender …

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 …

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 …

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 …

Session-based recommender systems (SBRSs) have shown superior performance over
conventional methods. However, they show limited scalability on large-scale industrial …