Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.

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Introduction to Recommender Systems Handbook

The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we aim to mine interesting locations and classical travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG). Second, based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location. This model infers the interest of a location by taking into account the following three factors. 1) The interest of a location depends on not only the number of users visiting this location but also these users' travel experiences. 2) Users' travel experiences and location interests have a mutual reinforcement relationship. 3) The interest of a location and the travel experience of a user are relative values and are region-related. Third, we mine the classical travel sequences among locations considering the interests of these locations and users' travel experiences. We evaluated our system using a large GPS dataset collected by 107 users over a period of one year in the real world. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, when considering the users' travel experiences and location interests, we achieved a better performance beyond baselines, such as rank-by-count and rank-by-interest, etc.

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Mining interesting locations and travel sequences from GPS trajectories

"Gamification" is an informal umbrella term for the use of video game elements in non-gaming systems to improve user experience (UX) and user engagement. The recent introduction of 'gamified' applications to large audiences promises new additions to the existing rich and diverse research on the heuristics, design patterns and dynamics of games and the positive UX they provide. However, what is lacking for a next step forward is the integration of this precise diversity of research endeavors. Therefore, this workshop brings together practitioners and researchers to develop a shared understanding of existing approaches and findings around the gamification of information systems, and identify key synergies, opportunities, and questions for future research.

http://gamification-research.org/wp-content/uploads/2011/04/01-Deterding-Sicart-Nacke-OHara-Dixon.pdf

Gamification. using game-design elements in non-gaming contexts

A system for performing collaborative tasks which permits collaborators to determine the form of a model of the collaborative work, to modify the model in the course of the collaborative work, and to use the model to access information related to the collaborative work. A graphical user interface permits collaborators to view the model and the information accessible via the model in various ways as well as to modify the model and the information. The information related to the collaborative work includes documents, discussions, email, reminders, and alerts. In a disclosed embodiment of the system, the model is made up of model entities which belong to hierarchies. A model entity may be a member of more than one hierarchy and the different hierarchies are used to provide different views of the model.

System for performing collaborative tasks

A system for supporting collaborative activity in a network includes a storage component storing data related to the network and a model of the network; a processor that accesses the stored data and the model to process the data according to the model, where the stored data relates to the collaborative activity including user-defined data created by interaction of a user and the model, and context data related to the user, where the user-defined data and the context data, as metadata, are stored in the storage component; and a user interface, provided by the processor, that presents the user-defined data, the context data, and the model in a form readable by the user, the interface permitting the user to define a set of arbitrary domains, relate the user-defined data to the domains, and view relationships between the user-defined data and the context data, and the domains.

System and method for supporting collaborative activity

The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures or physical manipulation of the devices. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. Unlike statistical methods, uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures and physical manipulations. We evaluate uWave using a large gesture library with over 4000 samples collected from eight users over an elongated period of time for a gesture vocabulary with eight gesture patterns identified by a Nokia research. It shows that uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. Our evaluation data set is the largest and most extensive in published studies, to the best of our knowledge. We also present applications of uWave in gesture-based user authentication and interaction with three-dimensional mobile user interfaces using user created gestures.

uWave: Accelerometer-based personalized gesture recognition and its applications

Internet-based recommender systems have traditionally employed collaborative filtering techniques to deliver relevant "digital" results to users. In the mobile Internet however, recommendations typically involve "physical" entities (e.g., restaurants), requiring additional user effort for fulfillment. Thus, in addition to the inherent requirements of high scalability and low latency, we must also take into account a "convenience" metric in making recommendations. In this paper, we propose an enhanced collaborative filtering solution that uses location as a key criterion for generating recommendations. We frame the discussion in the context of our "restaurant recommender" system, and describe preliminary results that indicate the utility of such an approach. We conclude with a look at open issues in this space, and motivate a future discussion on the business impact and implications of mining the data in such systems.

Using location for personalized POI recommendations in mobile environments

Hitting the distributed computing sweet spot with TSpaces

In this work, we study how Twitter can be used as a sensor to detect frequent and diverse social and physical events in real-time. We devise efficient data collection and event recognition solutions that work despite various limits on free access to Twitter data. We describe a web service implementation of our solution and report our experience with the 2010-2011 US National Football League (NFL) games. The service was able to recognize NFL game events within 40 seconds and with accuracy up to 90%. This capability will be very useful for not only real-time electronic program guide for live broadcast programs but also refined auction of advertisement slots. More importantly, it demonstrates for the first time the feasibility of using Twitter for real-time social and physical event detection for ubiquitous computing.

Human as Real-Time Sensors of Social and Physical Events: A Case Study of Twitter and Sports Games

A method in a wireless communication device including requesting contextual information from a source outside the wireless communication device, the contextual information requested by a context aware application on the wireless communications device, and providing alternate contextual information to the context aware application with a contextual information discriminator of the wireless communications device when contextual information from the source from which contextual information is requested is not available.

Venugopal Vasudevan

Papers

uWave: Accelerometer-based personalized gesture recognition and its applications

Using location for personalized POI recommendations in mobile environments

Hitting the distributed computing sweet spot with TSpaces

Human as Real-Time Sensors of Social and Physical Events: A Case Study of Twitter and Sports Games

Contextual information management in wireless communications devices and methods therefor