Nowadays, video games play a very important role in human life and no longer purely associated with escapism or entertainment. In fact, gaming has become an essential part of our daily routines, which give rise to the exponential growth of various online game platforms. By participating in such platforms, individuals generate a multitude of game data points, which, for example, can be further used for automatic user profiling and recommendation applications. However, the literature on automatic learning from the game data is relatively sparse, which had inspired us to tackle the problem of player profiling in this first preliminary study. Specifically, in this work, we approach the task of player gender prediction based on various types of game data. Our initial experimental results inspire further research on user profiling in the game domain.

Venue category recommendation is an essential application for the tourism and advertisement industries, wherein it may suggest attractive localities within close proximity to users' current location. Considering that many adults use more than three social networks simultaneously, it is reasonable to leverage on this rapidly growing multi-source social media data to boost venue recommendation performance. Another approach to achieve higher recommendation results is to utilize group knowledge, which is able to diversify recommendation output. Taking into account these two aspects, we introduce a novel cross-network collaborative recommendation framework C3R, which utilizes both individual and group knowledge, while being trained on data from multiple social media sources. Group knowledge is derived based on new cross-source user community detection approach, which utilizes both inter-source relationship and the ability of sources to complement each other. To fully utilize multi-source multi-view data, we process user-generated content by employing state-of-the-art text, image, and location processing techniques. Our experimental results demonstrate the superiority of our multi-source framework over state-of-the-art baselines and different data source combinations. In addition, we suggest a new approach for automatic construction of inter-network relationship graph based on the data, which eliminates the necessity of having pre-defined domain knowledge.

In this technical demonstration, we propose a cloud-based Big Data Platform for Social Multimedia Analytics called bBridge that automatically detects and profiles meaningful user communities in a specified geographical region, followed by rich analytics on communities' multimedia streams. The system executes a community detection approach that considers the ability of social networks to complement each other during the process of latent representation learning, while the community profiling is implemented based on the state-of-the-art multi-modal latent topic modeling and personal user profiling techniques. The stream analytics is performed via cloud-based stream analytics engine, while the multi-source data crawler deployed as a distributed cloud jobs. Overall, the bBridge platform integrates all the above techniques to serve both business and personal objectives.