Willingness optimization for social group activity
نویسندگان
چکیده
منابع مشابه
Willingness Optimization for Social Group Activity
Studies show that a person is willing to join a social group activity if the activity is interesting, and if some close friends also join the activity as companions. The literature has demonstrated that the interests of a person and the social tightness among friends can be effectively derived and mined from social networking websites. However, even with the above two kinds of information widel...
متن کاملScale-Adaptive Group Optimization for Social Activity Planning
Studies have shown that each person is more inclined to enjoy a group activity when 1) she is interested in the activity, and 2) many friends with the same interest join it as well. Nevertheless, even with the interest and social tightness information available in online social networks, nowadays many social group activities still need to be coordinated manually. In this paper, therefore, we fi...
متن کاملSocial emotional optimization algorithm with group decision
Social emotional optimization algorithm (SEOA) is a new swarm intelligent technique by simulating human social behaviors. In SEOA, each individual represents a virtual person and pursues high society status by selecting the behaviors according to the corresponding emotional index in each iteration. Therefore, how to simulate the personal decision mechanism plays an important role for the algori...
متن کاملGroup Activity Selection on Social Networks
We propose a new variant of the group activity selection problem (GASP), where the agents are placed on a social network and activities can only be assigned to connected subgroups (gGASP). We show that if multiple groups can simultaneously engage in the same activity, finding a stable outcome is easy as long as the network is acyclic. In contrast, if each activity can be assigned to a single gr...
متن کاملRelevance Topic Model for Unstructured Social Group Activity Recognition
Unstructured social group activity recognition in web videos is a challenging task due to 1) the semantic gap between class labels and low-level visual features and 2) the lack of labeled training data. To tackle this problem, we propose a “relevance topic model” for jointly learning meaningful mid-level representations upon bagof-words (BoW) video representations and a classifier with sparse w...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the VLDB Endowment
سال: 2013
ISSN: 2150-8097
DOI: 10.14778/2732240.2732244