While existing social networking services tend to connect people who know each other, people show a desire to also connect to yet unknown people in physical proximity. Existing research shows that people tend to connect to similar people. Utilizing technology in order to stimulate human interaction between strangers, we consider the scenario of two strangers meeting. On the example of similarity in musical taste, we develop a solution for the problem of similarity estimation in proximity-based mobile social networks. We show that a single exchange of a probabilistic data structure between two devices can closely estimate the similarity of two users - without the need to contact a third-party server. We introduce metrics for fast and space-efficient approximation of the Dice coefficient of two multisets - based on the comparison of two Counting Bloom Filters or two Count-Min Sketches. Our analysis shows that utilizing a single hash function minimizes the error when comparing these probabilistic data structures. The size that should be chosen for the data structure depends on the expected average number of unique input elements. Using real user data, we show that a Counting Bloom Filter with a single hash function and a length of 128 is sufficient to accurately estimate the similarity between two multisets representing the musical tastes of two users. Our approach is generalizable for any other similarity estimation of frequencies represented as multisets.
Before the advent of smartphones, a similar idea of social networking was described in @cite_0 . Here, users exchange identifiers of existing OSNs with each other via Bluetooth and can look up each other's information on the OSN. Having the data already available on the smartphone gives us the possibility to directly compare data instead of relying on existing OSNs. Some other papers present similar ideas, utilizing Bluetooth and central servers @cite_1 or manually entered interests to find user similarities pietilainen_mobiclique:_2009 . also follows the idea to share data via Bluetooth and describes a so-called for finding the intersection of two sets of topics of interest @cite_18 . More recent work deals with proximity-based mobile social networking: with , the user can see profiles of other users in proximity and has to evaluate manually if he she is interested in another user without support for automatic similarity estimation @cite_6 . In , the devices of users with similar interests are used for forwarding messages @cite_5 , while there is no system to stimulate interactions between users or offer recommendations for new contacts.
@cite_18 @cite_1 @cite_6 @cite_0 @cite_5
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Small talk is an important social lubricant that helps people, especially strangers, initiate conversations and make friends with each other in physical proximity. However, due to difficulties in quickly identifying significant topics of common interest, real-world small talk tends to be superficial. The mass popularity of mobile phones can help improve the effectiveness of small talk. In this paper, we present E-SmallTalker, a distributed mobile communications system that facilitates social networking in physical proximity. It automatically discovers and suggests topics such as common interests for more significant conversations. We build on Bluetooth Service Discovery Protocol (SDP) to exchange potential topics by customizing service attributes to publish nonservice-related information without establishing a connection. We propose a novel iterative Bloom filter (IBF) protocol that encodes topics to fit in SDP attributes and achieves a low false positive rate. We have implemented the system in Java ME for ease of deployment. Our experiments on real-world phones show that it is efficient enough at the system level to facilitate social interactions among strangers in physical proximity. To the best of our knowledge, E-SmallTalker is the first distributed mobile system to achieve the same purpose. Many mobile devices incorporate low-power wireless connectivity protocols, such as Bluetooth, that can be used to identify an individual to other people nearby. We have developed an architecture that leverages this functionality in mobile phones - originally designed for communication at a distance - to connect people across the room. Serendipity is an application of the architecture. It combines the existing communications infrastructure with online introduction systems' functionality to facilitate interactions between physically proximate people through a centralized server. A new mobile-phone-based system uses Bluetooth hardware addresses and a database of user profiles to cue informal, face-to-face interactions between nearby users who don't know each other, but probably should. In this paper, we propose E-Shadow, a distributed mobile phone-based local social networking system. E-Shadow has two main components: (1) Local profiles. They enable E-Shadow users to record and share their names, interests, and other information with fine-grained privacy controls. (2) Mobile phone based local social interaction tools. E-Shadow provides mobile phone software that enables rich social interactions. The software maps proximate users’ local profiles to their human owners and enables user communication and content sharing. We have designed and implemented E-Shadow on mobile phones. In our E-Shadow system, we allow users to perform dynamic and layered information publishing, making use of interpersonal relevance in space and time. Our system also provides a mechanism to help users perform direction-driven localization of an E-Shadow and match it with its owner. Experiments on real world Windows Mobile phones and large-scale simulations show that our system disseminates information efficiently and helps receivers find the direction of a specific E-Shadow with accuracy. We believe our E-Shadow concept and system can lead to a more tightly-knit temporary community in one’s physical vicinity. One of the most compelling social questions, which until now was left unanswered by current technology, is "Who's that?" This question is usually asked about a new unfamiliar person in a social setting who piques your curiosity. This article presents WhozThat, a system that ties together online social networks with mobile smart- phones to answer this common and essential social question. WhozThat builds an infrastructure that shares social networking IDs locally, using wireless technology, while also leveraging a wireless connection to the Internet's social networks to bind identity with location. In addition, WhozThat offers an entire ecosystem on which increasingly complex context-aware applications can be built; that is, once the environment knows who one is, the environment can adapt its content based on the individual's identity or even the collective tastes of a local group. We describe a prototype of the basic WhozThat system and also describe a more advanced service we built, a context-aware music player. We also discuss security and privacy issues introduced by mobile social networks. In this paper we describe SANE, the first forwarding mechanism that combines the advantages of both social-aware and stateless approaches in pocket switched network routing. SANE is based on the observation“that we validate on real-world traces”that individuals with similar interests tend to meet more often. In our approach, individuals (network members) are characterized by their interest profile, a compact representation of their interests. Through extensive experiments, we show the superiority of social-aware, stateless forwarding over existing stateful, social-aware and stateless, social-oblivious forwarding. An important byproduct of our interest-based approach is that it easily enables innovative routing primitives, such as interest-casting. An interest-casting protocol is also described, and extensively evaluated through experiments based on both real-world and synthetic mobility traces.