社會影響力會使個人固有的核心偏好在團體互動過程中，動態轉化成行為偏好，這在團體推薦系統中扮演很重要的角色。個體的易感性、社會關係與情境皆是驅動社會影響力的核心因子，系統需綜觀考量以提供更有效的推薦。然而，傳統基於歷史數據來計算的方法常面臨冷啟動、問卷偏誤以及無法動態適應情境等挑戰。為此，本研究採用設計科學研究方法，將現有的微時刻推薦系統設計原則拆解成更適用於臨時群體推薦的機制，透過分析團體互動過程中的行為，以預測個人在互動過程中行為偏好的轉變，在系統中提供三項即時更新的推薦清單。此外，我們在系統提供團體如總結、推薦、自動推播等等的輔助機制，幫助團體更有效的達成共識。研究結果顯示，相較於考慮核心偏好，在互動過程透過行為抓取的行為偏好更能對齊使用者當下的真實偏好。

Social influence can dynamically transform individuals’ inherent core preferences into behavioral preferences during group interaction, and this process plays an important role in group recommender systems. Individual susceptibility, social relationships, and contextual factors are key factors that drive social influence, and these factors need to be considered comprehensively to provide more effective recommendations. However, traditional approaches based on historical data often face challenges such as cold-start problems, questionnaire bias, and an inability to dynamically adapt to changing contexts.
This study adopts a design science research approach and decomposes existing design principles for micro-moment recommender systems into mechanisms that are more suitable for ephemeral group recommendation. By analyzing users’ behaviors during group interaction, the system predicts changes in individuals’ behavioral preferences throughout the interaction process and provides three recommendation lists that are updated in real time. In addition, the system provides supporting mechanisms such as summaries, recommendations, and proactive recommendation delivery to help groups reach consensus more effectively.
The results show that, compared with core preferences, behavioral preferences captured from users’ behaviors during interaction are better aligned with users’ actual preferences at the moment.

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