Consensus-aware Contrastive Learning for Group Recommendation

Group recommendation aims to provide personalized item suggestions to a group of users by reflecting their collective preferences. A fundamental challenge in this task is deriving a consensus that adequately represents the diverse interests of individual group members. Despite advancements made by deep learning-based models, existing approaches still struggle in two main areas: (1) Capturing consensus in small-group settings, which are more prevalent in real-world applications, and (2) Balancing individual preferences with overall group performance, particularly in hypergraph-based methods that tend to emphasize group accuracy at the expense of personalization. To address these challenges, we introduce a Consensus-aware Contrastive Learning for Group Recommendation (CoCoRec) that models group consensus through contrastive learning. CoCoRec utilizes a transformer encoder to jointly learn user and group representations, enabling richer modeling of intra-group dynamics. Additionally, the contrastive objective helps reduce overfitting from high-frequency user interactions, leading to more robust and representative group embeddings. Experiments conducted on four benchmark datasets show that CoCoRec consistently outperforms state-of-the-art baselines in both individual and group recommendation scenarios, highlighting the effectiveness of consensus-aware contrastive learning in group recommendation tasks.

Temporal Information and Event Markup Language: TIE-ML Markup Process and Schema Version 1.0

Temporal Information and Event Markup Language (TIE-ML) is a markup strategy and annotation schema to improve the productivity and accuracy of temporal and event related annotation of corpora to facilitate machine learning based model training. For the annotation of events, temporal sequencing, and durations, it is significantly simpler by providing an extremely reduced tag set for just temporal relations and event enumeration. In comparison to other standards, as for example the Time Markup Language (TimeML), it is much easier to use by dropping sophisticated formalisms, theoretical concepts, and annotation approaches. Annotations of corpora using TimeML can be mapped to TIE-ML with a loss, and TIE-ML annotations can be fully mapped to TimeML with certain under-specification.