he mobile social networking revolution is upon us and could have as profound an effect in enriching local social interaction as the Internet has had in enriching online information access and discourse. The key observation in this article is that the explosive phenomenon of online social networks can be harnessed using mobile devices to answer the compelling question that frequently appears in local social contexts: "Who's that?" It is often the case that people want to find out more about those who are around them; for example, who is that speaking to a group of people in a corner of the room, or who is that who just walked into the room? Standard solutions include asking those around you, looking at name tags, introducing yourself, and so on, none of which leverage the power of technology to help answer these compelling questions and thereby enrich the social interaction.Online social networks have exploded in popularity [1-3]. As of December 2007, Facebook had over 59 million users [4]. It is estimated that over 85 percent of four-year college students have a Facebook profile, presenting a very usable penetration rate and providing an incredible resource for applications that might leverage this data. These online social networks provide a wealth of personal contextual information, including name, picture, contact information, gender, relationship status/interests, activities/hobbies, musical preferences, literature interests, group membership, and, of course, friendship information concerning user interconnection. Social networks provide a variety of mechanisms for users to share these rich sets of contextual data with other users, including searching for other users with similar interests, as well as a means to establish and maintain communication with other users. Social networks can be seen as a natural evolution of the Internet, where the first big wave facilitated a person's access to information; for example, Web servers and peer-topeer networks providing news and information content, as well as ways to buy products, whereas this next big wave is focused on facilitating person-to-person communication.WhozThat is motivated by the idea that bringing this rich contextual information from online social networks into the real world of local human interactions substantially enriches local social interaction. Imagine if you knew more about the people around you in a social gathering, such that you could more easily strike up a conversation with someone with whom you were interested in talking. By being informed via mobile technology of the identity of the person with whom you are seeking to interact and consulting information obtained from that person's public social networking profile, you could more easily initiate a conversation, perhaps introducing yourself and saying, "I noticed we have a shared interest in this hobby or that cause." The ability of mobile social networking (MoSoNet) technology to substantially lower the barriers to social discourse by minimizing unfamiliarity could revolutionize human soc...

Group recommendation, which makes recommendations to a group of users instead of individuals, has become increasingly important in both the workspace and people's social activities, such as brainstorming sessions for coworkers and social TV for family members or friends. Group recommendation is a challenging problem due to the dynamics of group memberships and diversity of group members. Previous work focused mainly on the content interests of group members and ignored the social characteristics within a group, resulting in suboptimal group recommendation performance.In this work, we propose a group recommendation method that utilizes both social and content interests of group members. We study the key characteristics of groups and propose (1) a group consensus function that captures the social, expertise, and interest dissimilarity among multiple group members; and (2) a generic framework that automatically analyzes group characteristics and constructs the corresponding group consensus function. Detailed user studies of diverse groups demonstrate the effectiveness of the proposed techniques, and the importance of incorporating both social and content interests in group recommender systems.

Determinantal point processes (DPPs) have attracted significant attention as an elegant model that is able to capture the balance between quality and diversity within sets. DPPs are parameterized by a positive semi-definite kernel matrix. While DPPs have substantial expressive power, they are fundamentally limited by the parameterization of the kernel matrix and their inability to capture nonlinear interactions between items within sets. We present the deep DPP model as way to address these limitations, by using a deep feed-forward neural network to learn the kernel matrix. In addition to allowing us to capture nonlinear item interactions, the deep DPP also allows easy incorporation of item metadata into DPP learning. Since the learning target is the DPP kernel matrix, the deep DPP allows us to use existing DPP algorithms for efficient learning, sampling, and prediction. Through an evaluation on several real-world datasets, we show experimentally that the deep DPP can provide a considerable improvement in the predictive performance of DPPs, while also outperforming strong baseline models in many cases.Preprint. Under review.

In this paper, we identify mobile social networks as an important new direction of research in mobile computing, and show how an expanded definition of mobile social networks that includes sensor networks can enable exciting new contextaware applications, such as context-aware video screens, music jukeboxes, and mobile health applications. We offer SocialFusion as a system capable of systematically integrating such diverse mobile, social, and sensing input streams and effectuating the appropriate context-aware output action. We explain some of the major challenges that SocialFusion must overcome. We describe some preliminary results that we have obtained in implementing the SocialFusion vision.

Augmented reality (AR) applications have recently become popular on modern smartphones. We explore the effectiveness of this mobile AR technology in the context of grocery shopping, in particular as a means to assist shoppers in making healthier decisions as they decide which grocery products to buy. We construct an AR-assisted mobile grocery-shopping application that makes real-time, customized recommendations of healthy products to users and also highlights products to avoid for various types of health concerns, such as allergies to milk or nut products, low-sodium or low-fat diets, and general caloric intake. We have implemented a prototype of this AR-assisted mobile grocery shopping application and evaluated its effectiveness in grocery store aisles. Our application's evaluation with typical grocery shoppers demonstrates that AR overlay tagging of products reduces the search time to find healthy food items, and that coloring the tags helps to improve the user's ability to quickly and easily identify recommended products, as well as products to avoid. We have evaluated our application's functionality by analyzing the data we collected from 15 in-person actual grocery-shopping subjects and 104 online application survey participants.