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A survey on session-based recommender systems
Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …
consumption, services, and decision-making in the overloaded information era and digitized …
Sequential recommender systems: challenges, progress and prospects
The emerging topic of sequential recommender systems has attracted increasing attention in
recent years. Different from the conventional recommender systems including collaborative …
recent years. Different from the conventional recommender systems including collaborative …
RLAS‐BIABC: a reinforcement learning‐based answer selection using the bert model boosted by an improved ABC algorithm
Answer selection (AS) is a critical subtask of the open‐domain question answering (QA)
problem. The present paper proposes a method called RLAS‐BIABC for AS, which is …
problem. The present paper proposes a method called RLAS‐BIABC for AS, which is …
Enhancing sequential recommendation with contrastive generative adversarial network
Sequential recommendation models a user's historical sequence to predict future items.
Existing studies utilize deep learning methods and contrastive learning for data …
Existing studies utilize deep learning methods and contrastive learning for data …
Chinese medical question answer selection via hybrid models based on CNN and GRU
Question answer selection in the Chinese medical field is very challenging since it requires
effective text representations to capture the complex semantic relationships between …
effective text representations to capture the complex semantic relationships between …
Data science for next-generation recommender systems
Data science has been the foundation of recommender systems for a long time. Over the
past few decades, various recommender systems have been developed using different data …
past few decades, various recommender systems have been developed using different data …
Representation learning with multi-level attention for activity trajectory similarity computation
Massive trajectory data stem from the prevalence of equipment-supporting GPS and
wireless communication technology. Especially, activity trajectory from Location-based …
wireless communication technology. Especially, activity trajectory from Location-based …
Concept representation by learning explicit and implicit concept couplings
Generating the precise semantic representation of a word or concept is a fundamental task
in natural language processing. Recent studies which incorporate semantic knowledge into …
in natural language processing. Recent studies which incorporate semantic knowledge into …
Hierarchical attentive transaction embedding with intra-and inter-transaction dependencies for next-item recommendation
A transaction-based recommender system (TBRS) aims to predict the next item by modeling
dependencies in transactional data. Generally, two kinds of dependencies considered are …
dependencies in transactional data. Generally, two kinds of dependencies considered are …
Modelling local and global dependencies for next-item recommendations
Session-based recommender systems (SBRSs) aim at predicting the next item by modelling
the complex dependencies within and across sessions. Most of the existing SBRSs make …
the complex dependencies within and across sessions. Most of the existing SBRSs make …