3 research outputs found
Who wants to join me? Companion recommendation in location based social networks
We consider the problem of identifying possible companions for a user who is planning to visit a given venue. Specifically, we study the task of predicting which of the user's current friends, in a location based social network (LBSN), are most likely to be interested in joining the visit. An important underlying assumption of our model is that friendship relations can be clustered based on the kinds of interests that are shared by the friends. To identify these friendship types, we use a latent topic model, which moreover takes into account the geographic proximity of the user to the location of the proposed venue. To the best of our knowledge, our model is the first that addresses the task of recommending companions for a proposed activity. While a number of existing topic models can be adapted to make such predictions, we experimentally show that such methods are significantly outperformed by our model
Data-driven Computational Social Science: A Survey
Social science concerns issues on individuals, relationships, and the whole
society. The complexity of research topics in social science makes it the
amalgamation of multiple disciplines, such as economics, political science, and
sociology, etc. For centuries, scientists have conducted many studies to
understand the mechanisms of the society. However, due to the limitations of
traditional research methods, there exist many critical social issues to be
explored. To solve those issues, computational social science emerges due to
the rapid advancements of computation technologies and the profound studies on
social science. With the aids of the advanced research techniques, various
kinds of data from diverse areas can be acquired nowadays, and they can help us
look into social problems with a new eye. As a result, utilizing various data
to reveal issues derived from computational social science area has attracted
more and more attentions. In this paper, to the best of our knowledge, we
present a survey on data-driven computational social science for the first time
which primarily focuses on reviewing application domains involving human
dynamics. The state-of-the-art research on human dynamics is reviewed from
three aspects: individuals, relationships, and collectives. Specifically, the
research methodologies used to address research challenges in aforementioned
application domains are summarized. In addition, some important open challenges
with respect to both emerging research topics and research methods are
discussed.Comment: 28 pages, 8 figure