User interest profiles have become essential for personalizing information streams and services, and user interfaces and experiences. In today's world, social networks such as Facebook or Twitter provide users with a powerful platform for interest expression and can, thus, act as a rich content source for automated user interest modeling. This, however, poses significant challenges because the user generated content on them consists of free unstructured text. In addition, users may not explicitly post or tweet about everything that interests them. Moreover, their interests evolve over time. In this paper, we propose a novel unsupervised algorithm and system that addresses these challenges. It models a broad range of an individual user's explicit and implicit interests from her social network profile and activities without any user input. We perform extensive evaluation of our system, and algorithm, with a dataset consisting of 488 active Facebook users' profiles and demonstrate that it can accurately estimate a user's interests in practice.

Given the abundance of online information available to mobile users, particularly tourists and weekend travelers, recommender systems that effectively filter this information and suggest interesting participatory opportunities will become increasingly important. Previous work has explored recommending interesting locations; however, users would also benefit from recommendations for activities in which to participate at those locations along with suitable times and days. Thus, systems that provide collaborative recommendations involving multiple dimensions such as location, activities and time would enhance the overall experience of users.The relationship among these dimensions can be modeled by higher-order matrices called tensors which are then solved by tensor factorization. However, these tensors can be extremely sparse. In this paper, we present a system and an approach for performing multi-dimensional collaborative recommendations for Who (User), What (Activity), When (Time) and Where (Location), using tensor factorization on sparse user-generated data. We formulate an objective function which simultaneously factorizes coupled tensors and matrices constructed from heterogeneous data sources. We evaluate our system and approach on large-scale real world data sets consisting of 588,000 Flickr photos collected from three major metro regions in USA. We compare our approach with several state-of-the-art baselines and demonstrate that it outperforms all of them.

In order to make context-aware systems more effective and provide timely, personalized and relevant information to a user, the context or situation of the user must be clearly defined along several dimensions. To this end, the system needs to simultaneously recognize multiple dimensions of the user'

We describe an abstract, generic and extensible ontology, the Rover Context Model Ontology (RoCoMO), which is currently being designed and developed to model and represent context in an intelligent context-aware middleware system, called Rover II. The Rover Context Model (RoCoM) is the underlying context model for Rover II and is centered on four primitives that can be used to represent a situation: entity, event, activity and relationship. The ontology is expressed using the Web Ontology Language (OWL) and includes two components -- RoCoM Core Ontology and the RoCoM Application Ontology. We also illustrate its usage with the aid of a public safety application called M-Urgency that is currently deployed at the UMD campus.

A major challenge of context models is to balance simplicity, generality, usability and extensibility. It is also important that the model be practical and implementable. In pursuit of this goal, this paper proposes a context model, Rover Context Model (RoCoM), structured around four primitives that can be used to represent and model any situation and activity: entities, events, relationships, and activities. It introduces the notion of templates of context for each primitive and describes, albeit briefly, the RoCoM Ontology (RoCoMO). It also describes the design and architecture of an abstract, generic and intelligent context-aware middleware called Rover II. We propose this framework as a solution to address the context problem as a whole, and be usable in many domains. We also illustrate its application with the aid of a context-aware public safety application that is deployed in the UMD campus.

The importance of information for the appropriate handling of any situation is well recognized. We, as human beings, use context not only to interpret available information, but also to seek additional relevant, missing information. Most information systems, which support human decision-making, process context inadequately. Moreover, when some context is considered, the structure is often so rigid that, but for very restricted applications, such systems are not usable. In order to integrate context-handling capabilities with an information integration system, we need a context model for representing context that can efficiently acquire, maintain and integrate contextual information and make it available to applications on demand. In this paper, we present such a model and describe the design of Rover II, a situation-handling platform which supports integration of context-aware applications. The system manages context by constructing a situation graph which dynamically reflects the relevant situation information. We illustrate the use of Rover II with M-Urgency, a public safety application providing audio and video support for emergency help. This system is being deployed for the University of Maryland campus community of 44 000 students, faculty and staff.