Probabilistic Personalized Recommendation Models For Heterogeneous Social Data

Content recommendation has risen to a new dimension with the advent of platforms like Twitter, Facebook, FriendFeed, Dailybooth, and Instagram. Although this uproar of data has provided us with a goldmine of real-world information, the problem of information overload has become a major barrier in developing predictive models. Therefore, the objective of this The- sis is to propose various recommendation, prediction and information retrieval models that are capable of leveraging such vast heterogeneous content. More specifically, this Thesis focuses on proposing models based on probabilistic generative frameworks for the following tasks: (a) recommending backers and projects in Kickstarter crowdfunding domain and (b) point of interest recommendation in Foursquare. Through comprehensive set of experiments over a variety of datasets, we show that our models are capable of providing practically useful results for recommendation and information retrieval tasks

Performance Evaluation Of Voice Chat In Vechicular Ad-Hoc Networks

Inter vehicle communication has emerged as an important area of research. With a rapid evolution of social networks, people are constantly looking for social interactions in all types of mobile environment. In this Thesis, we propose a voice chat model for Vehicle-to-Vehicle (V2V) communication that mimics a real-world group talk scenario, and measure its performance using Flood and Scalable broadcast (SBA) protocols. To evaluate the performance of the voice chat, we use different parameters such as group size, network density, transmission range and hop counts to show that our voice chat application is highly feasible in VANET environment. Furthermore, we perform a thorough comparison of Flood and SBA broadcast protocols throughout our simulation. Contrary to the performance of SBA in low speed ad-hoc networks, we show that the Flood broadcast algorithm has better content delivery than SBA in all scenarios that we tested. We implement our model using the NS2 network simulator using a realistic vehicular trace that depicts the movement of vehicles in the I-75S freeway from Detroit to Toledo