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Group Dynamics: The Psychology of Small Group Behavior
... Group definition: Group is an interdisciplinary concept, many subjects define group from different perspectives. Group has drawn significant attention in the field of sociology, Ark V [40] defined that group is not the sum of individuals, group members must exist interaction and influence each other. Based on this definition of group, we can hold that group members have different influence in our work. ...
Frequent group activities of human beings have become an indispensable part in their daily life. Group recommendation can recommend satisfactory activities to group members in the recommender systems, and the key issue is how to aggregate preferences in different group members. Most existing group recommendation employed the predefined static aggregation strategies to aggregate the preferences of different group members, but these static strategies cannot simulate the dynamic group decision-making. Meanwhile, most of these methods depend on intuitions or assumptions to analyze the influence of group members and lack of convincing theoretical support. We argue that the influence of group members plays a particularly important role in group decision-making and it can better assist group profile modeling and perform more accurate group recommendation. To tackle the issue of preference aggregation for group recommendation, we propose a novel attentive aggregation representation learning method based on sociological theory for group recommendation, namely SIAGR (short for "Social Influence-based Attentive Group Recommendation"), which takes attention mechanisms and the popular method (BERT) as the aggregation representation for group profile modeling. Specifically, we analyze the influence of group members based on social identity theory and two-step flow theory and exploit an attentive mavens mining method. In addition, we develop a BERT-based representation method to learn the interaction of group members. Lastly, we complete the group recommendation under the neural collaborative filtering framework and verify the effectiveness of the proposed method by experimenting.
In the context of the Bitcoin market and its virtual investment communities, this paper demonstrates that both similarity and dissimilarity in social media messages’ contents can be associated with high quotation probability among them. This finding advocates new understandings beyond the mainstream conclusion that network connections increase with similarity. It is also found that message networks offer significant implications to distinguish between accurate information and noise. Evidence shows that popular messages frequently quoted by others and redundant messages confirmed from different sources contain more accurate information for Bitcoin market predictions. Theoretical contributions and practical implications are discussed.
The notion of 'information system' is examined particularly from the perspective that those concerned with such 'systems' might learn something from the whole body of ideas concerned with the notion 'system'. The first section examines briefly the possible ...
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