Xuelian Long

A HITS-based POI recommendation algorithm for location-based social networks

Location-Based Social Networks (LBSNs), (also called as Geo-Social Networks), has been attracting more and more users by providing services that integrate social activities with location information. LBSN systems usually provide support for indicating various Points of Interest (POIs) but there is no straightforward rating mechanism for POIs in most LBSNs [1]. POI recommendations in LBSNs, thus, is an important and challenging research topic. In this paper, we first investigate the dataset crawled from Foursquare to explore the features that attract and influence users to check in at various POIs. Based on the analysis results, we propose a HITS (Hypertext Induced Topic Search)-based POI recommendation algorithm to recommend POIs to LBSN users that can also incorporate the impact of the social relationships on recommendations. We evaluate our proposed model on Foursquare dataset and compare our results with the latest POI recommendation algorithm. The experimental results show that our approach performs better.

BaRMS: A Bayesian Reputation Management Approach for P2P Systems

Current distributed Peer-to-Peer (P2P) applications offer a variety of flexible and convenient services through the Internet to users from different geographic locations and also support enhanced communications and interactions among them. However, security and trust are the key concerns in such applications as users in such an environment are typically unknown to each other. Trust management systems aim to decrease the risks in such applications and protect benign users from malicious users. In this paper, we introduce six attack models and propose a novel Bayesian Reputation Management System (BaRMS) for P2P environments using Bayesian probability and Markov Chain theories. BaRMS handles both positive and negative feedback. Through a case study, we show that this approach is better than the existing EigenTrust framework for P2P systems. Moreover, our simulation results of a P2P file sharing system also show that the proposed algorithm can greatly improve the performance over a system that does not include a trust management service under various attack models. We show that our proposed Bayesian Reputation Computation Algorithm (BaRCA) performs better than the EigenTrust algorithm under various models.

Enhanced One-Pass IP Multimedia Subsystem Authentication Protocol for UMTS

Universal Mobile Telecommunications System (UMTS) can support IP Multimedia services by including the IP Multimedia Subsystem (IMS) as part of its core network. To use IMS services, a user equipment needs to first authenticate itself with the UMTS and then with the IMS. However, these two authentication protocols share many similar operations. Recent research efforts have highlighted this issue and hence researchers have proposed one-pass authentication protocols to reduce the number of such overlapping steps and to address security vulnerabilities in the original IMS protocol. In this paper, we propose an enhanced one-pass authentication protocol that addresses the weaknesses of the two previously proposed one-pass authentication protocols. We also provide comparative analysis of our proposed work with the existing approaches in terms of performance, security and compatibility.

Characterizing users' check-in activities using their scores in a location-based social network

Analysis of users’ check-ins in location-based social networks (LBSNs, also called GeoSocial Networks), such as Foursquare and Yelp, is essential to understand users’ mobility patterns and behaviors. However, most empirical results of users’ mobility patterns reported in the current literature are based on users’ sampled and nonconsecutive public check-ins. Additionally, such analyses take no account of the noise or false information in the dataset, such as dishonest check-ins created by users. These empirical results may be biased and hence may bring side effects to LBSN services, such as friend and venue recommendations. Foursquare, one of the most popular LBSNs, provides a feature called a user’s score. A user’s score is an aggregate measure computed by the system based on more accurate and complete check-ins of the user. It reflects a snapshot of the user’s temporal and spatial patterns from his/her check-ins. For example, a high user score indicates that the user checked in at many venues regularly or s/he visited a number of new venues. In this paper, we show how a user’s score can be used as an alternative way to investigate the user’s mobility patterns. We first characterize a set of properties from the time series of a user’s consecutive weekly scores. Based on these properties, we identify different types of users by clustering users’ common check-in patterns using non-negative matrix factorization (NMF). We then analyze the correlations between the social features of user clusters and users’ check-in patterns. We present several interesting findings. For example, users with high scores (more mobile) tend to have more friends (more social). Our empirical results demonstrate how to uncover interesting spatio-temporal patterns by utilizing the aggregate measures released by a LBSN service.

BaRMS: A Bayesian Reputation Management approach for P2P systems

Current distributed applications offer a variety of flexible and convenient services through the Internet to users from different geographic locations and also support communications among them. However, security and trust are key concerns in such applications as users in such an environment are unknown to each other. Trust management systems aim to decrease the risks in such applications and protect benign users from malicious users. In this paper, we propose a novel Bayesian Reputation Management System (BaRMS) for Peer-to-Peer (P2P) environments using Bayesian probability and Markov Chain theories. BaRMS handles negative feedbacks and through a case study, we show that this approach is better than the existing EigenTrust framework for P2P systems. Moreover, our simulation results of a P2P file sharing system also show that the proposed algorithm can greatly improve the performance over a system that does not include a trust management service. We show that our proposed Bayesian Reputation Computation Algorithm (BaRCA) performs better than the EigenTrust algorithm.