Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users’ preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e.g., multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user’s check-in on a location as a Multi-center Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly.

In recent times, crowdsourcing over social networks has emerged as an active tool for complex task execution. In this paper, we address the problem faced by a planner to incentivize agents in the network to execute a task and also help in recruiting other agents for this purpose. We study this mechanism design problem under two natural resource optimization settings: (1) cost critical tasks, where the planner's goal is to minimize the total cost, and (2) time critical tasks, where the goal is to minimize the total time elapsed before the task is executed. We define a set of fairness properties that should be ideally satisfied by a crowdsourcing mechanism. We prove that no mechanism can satisfy all these properties simultaneously. We relax some of these properties and define their approximate counterparts. Under appropriate approximate fairness criteria, we obtain a non-trivial family of payment mechanisms. Moreover, we provide precise characterizations of cost critical and time critical mechanisms.

The increasing availability of “utility computing” resources such as clouds, grids, and massively parallel shared clusters can provide practically unlimited processing and memory capacity on demand, at some cost per unit of resource usage. This requires a new perspective in the design and evaluation of parallel search algorithms. Previous work in parallel search implicitly assumed ownership of a cluster with a static amount of CPU cores and RAM, and emphasized wallclock runtime. With utility computing resources, trade-offs between performance and monetary costs must be considered. This paper considers dynamically increasing the usage of utility computing resources until a problem is solved. Efficient resource allocation policies are analyzed in comparison with an optimal allocation strategy. We evaluate our iterative allocation strategy by applying it to the HDA* parallel search algorithm. The experimental results validate our theoretical predictions. They show that, in practice, the costs incurred by iterative allocation are reasonably close to an optimal (but a priori unknown) policy, and are significantly better than the worst-case analytical bounds.

We will like to call cities that enable such capabilities as, "semantic cities." In a semantic city, available resources are harnessed safely, sustainably and efficiently to achieve positive, measurable economic and societal outcomes. Enabling city information as a utility, through a robust (expressive, dynamic, scalable) and (critically) a sustainable technology and socially synergistic ecosystem could drive significant benefits and opportunities. Data (and then information and knowledge) from people, systems, and things is the single most scalable resource available to city stakeholders to reach the objective of semantic cities. Two major trends are supporting semantic cities -- open data and semantic web.

In a Smarter City, available resources are harnessed safely, sustainably and efficiently to achieve positive, measurable economic and societal outcomes. Data and information from people, systems and things is the single most scalable resource available to city stakeholders but difficult to publish, organize, discover and consume, especially in a real-time context. Enabling city information as a utility, through a robust (expressive, dynamic, scalable) and (critically) a sustainable technology and socially synergistic ecosystem, could drive significant benefits and opportunities. In the context of stream data (as real-time, gigantic, noisy and private data), this paper targets research issues we identify as important to harness the fused information resources of cities, Citizens and Stakeholders to reach the concept of Smarter Cities.

Smartphones incorporate many diverse and powerful sensors, which creates exciting new opportunities for data mining and human-computer interaction. In this paper we show how standard classification algorithms can use labeled smartphone-based accelerometer data to identify the physical activity a user is performing. Our main focus is on evaluating the relative performance of impersonal and personal activity recognition models. Our impersonal (i.e., universal) models are built using training data from a panel of users and are then applied to new users, while our personal models are built with data from each user and then applied only to new data from that user. Our results indicate that the personal models perform dramatically better than the impersonal models—even when trained from only a few minutes worth of data. These personal models typically even outperform hybrid models that utilize both personal and impersonal data. These results strongly argue for the construction of personal models whenever possible. Our research means that we can unobtrusively gain useful knowledge about the habits of potentially millions of users. It also means that we can facilitate human computer interaction by enabling the smartphone to consider context and this can lead to new and more effective applications.

The core business of Mobile Network Operators (MNO) has moved from network management and phone services to service providing. In contrast to Information Communication Technology (ICT) service providers, MNOs handle large amounts of their customers’ context data and generated content, which can be used to bring value-added services to customers and therefore, generate solid revenues. Given this scenario, this paper describes how Telecom Italia (a major Italian MNO) has prototyped such type of services after a deep research performed in the context-awareness and context management field and using its user-generated content management facilities in federation with other platforms and systems.

Knowledge workers work on many different tasks and must often switch between those tasks. In earlier work, we have shown the benefits of automatically capturing contexts for tasks for a specific category of knowledge worker, software programmers. Captured contexts facilitate task switches and reduce information overload by enabling the display of only the information relevant to the task-at-hand. In this paper, we describe the results of two studies of the use of captured contexts for a broad range of knowledge workers. The first study we describe is a field study of eight knowledge workers who used the model in their daily work for up to 25 days on tasks involving both file and web documents. We found that these knowledge workers need information to decay from their context and that our model is adequate at automatically trimming contexts. The second study is a case study of the use of contexts to support the operations of a software development company. We analyzed task contexts from hundreds of days of work from three users and found similar trends of information decaying from contexts. Results from each study also shed more light on the nature of mixed artifact task contexts.

This paper presents a prototype system called SFplanner which uses an automated planning technique to generate workflows for reconfiguring a computing infrastructure. The system allows an administrator to specify a configuration task which consists of current state, desired state and global constraints. This task is compiled to a grounded finite-domain representation as the input for the standard (unmodified) Fast-Downward planner in order to automatically generate a workflow. The execution of the workflow will bring the system into the desired state, preserving the global constraints at every stage of the workflow.

Homophily is the tendency of individuals in a social system to link to others who are similar to them and understanding homophily can help us build better user models for personalization and recommender systems. Many studies have verified homophily along demographic dimensions, such as age, location, occupation, etc., not only in real-world social networks but also online. However, there is limited research showing that homophily also exists when similarity is judged by topics of expertise or interests. We demonstrate the existence of topical homophily on Twitter using a novel source of evidence provided by Twitter lists. In this paper, we use LDA to extract topics from Twitter lists (a collection of user accounts created by some user that others can follow) and measure similarity between listed users based on the learned topics. We show that topically similar users are more likely to be linked via a follow relationship than less similar users.