Finding co-solvers on Twitter, with a little help from Linked Data

In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com

Habitualisation: localisation without location data

This paper looks at identifying the locations of users from the Nokia MDC dataset throughout the day without taking into consideration location based data. By looking at a users habits and idiosyncrasies we determined the likelihood of a users location within known stay regions which we call habitats. The features used to determine location were extracted from a users interaction with the smart phone. None of the features contained a users locations or a users proximity to objects with known locations. Using a set of structured output support vector learning techniques we found that a users location with respect to the areas of typical activities is well predictable solely from daily routines and a smart phone usage habits

Location Prediction: Communities Speak Louder than Friends

Humans are social animals, they interact with different communities of friends to conduct different activities. The literature shows that human mobility is constrained by their social relations. In this paper, we investigate the social impact of a person's communities on his mobility, instead of all friends from his online social networks. This study can be particularly useful, as certain social behaviors are influenced by specific communities but not all friends. To achieve our goal, we first develop a measure to characterize a person's social diversity, which we term `community entropy'. Through analysis of two real-life datasets, we demonstrate that a person's mobility is influenced only by a small fraction of his communities and the influence depends on the social contexts of the communities. We then exploit machine learning techniques to predict users' future movement based on their communities' information. Extensive experiments demonstrate the prediction's effectiveness.Comment: ACM Conference on Online Social Networks 2015, COSN 201

A developed GPS trajectories data management system for predicting tourists' POI

One of the areas that have challenges in the use of internet of things (IoT) is the field of tourism and travel. The issue here is how to employ this technology to serve the tourism and managing the produced data. This work is focus on the use of tourists' trajectories that are collected from global positioning system (GPS) mobile sensors as a source of information. The aim of work is to predict preferred tourism places for tourists by tracking tourists' behavior to extract the tourism places that have been visited by such tourists. Density based clustering algorithm is mainly used to extract stay points and point of interest (POI). By projecting GPS location (for user and places) on the Google map, the type and name of places favored by the tourists are determined. K nearest neighbor (KNN) algorithm with haversine distance has been adopted to find the nearest places for tourists. The evaluation of the obtained results shows superior and satisfactory performance that can reach the objective behind this work