A Personality-Guided Preference Aggregator for Ephemeral Group Recommendation

TL;DR

The article presents elsarticle.cls, a reworked LaTeX document class designed for Elsevier submissions that minimizes package conflicts and improves formatting consistency. Built on the base article.cls, it maintains kernel command signatures while providing preprint and final formatting options (models 1+, 3+, 5+) and streamlined environments for lists and theorems, with natbib as the primary citation system. It also offers integrated installation guidance and a straightforward workflow to generate and deploy the class file (.cls) within a user's TEXMF tree. Overall, elsarticle.cls simplifies preparing manuscripts for Elsevier, enhances compatibility with common packages, and supports flexible front-matter and formatting options for different publication stages.

Abstract

Ephemeral group recommendation (EGR) aims to suggest items for a group of users who come together for the first time. Existing work typically consider individual preferences as the sole factor in aggregating group preferences. However, they neglect to take into account the importance of the individual inherent factors, such as personality, and thus fail to accurately simulate the group decision-making process. Additionally, these methods often struggle due to insufficient interactive records. To tackle these issues, a Personality-Guided Preference Aggregator (PEGA) is proposed, which guides the preference aggregation of group members based on their personalities, rather than relying solely on their preferences. Specifically, implicit personalities are first extracted from user reviews. Hyper-rectangles are then used to aggregate individual personalities to obtain the "Group Personality", which allows for the learning of personality distributions within the group. Subsequently, a personality attention mechanism is employed to aggregate group preferences, and a preference-based fine-tuning module is used to balance the weights of personality and preferences. The role of personality in this approach is twofold: (1) To estimate the importance of individual users in a group and provide explainability; (2) To alleviate the data sparsity issue encountered in ephemeral groups. Experimental results demonstrate that, on four real-world datasets, the PEGA model significantly outperforms related baseline models in terms of classification accuracy and interpretability. Moreover, empirical evidence supports the idea that personality plays a pivotal role in enhancing the performance of EGR tasks.