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Wang-Chien Lee

Bio: Wang-Chien Lee is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Wireless sensor network & Nearest neighbor search. The author has an hindex of 60, co-authored 366 publications receiving 14123 citations. Previous affiliations of Wang-Chien Lee include Ohio State University & Verizon Communications.


Papers
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Proceedings ArticleDOI
27 Oct 2013
TL;DR: This paper proposes a collaborative recommendation framework, called User Preference, Proximity and Social-Based Collaborative Filtering} (UPS-CF), to make location recommendation for mobile users in LBSNs and finds that preference derived from similar users is important for in-town users while social influence becomes more important for out-of- town users.
Abstract: Most previous research on location recommendation services in location-based social networks (LBSNs) makes recommendations without considering where the targeted user is currently located. Such services may recommend a place near her hometown even if the user is traveling out of town. In this paper, we study the issues in making location recommendations for out-of-town users by taking into account user preference, social influence and geographical proximity. Accordingly, we propose a collaborative recommendation framework, called User Preference, Proximity and Social-Based Collaborative Filtering} (UPS-CF), to make location recommendation for mobile users in LBSNs. We validate our ideas by comprehensive experiments using real datasets collected from Foursquare and Gowalla. By comparing baseline algorithms and conventional collaborative filtering approach (and its variants), we show that UPS-CF exhibits the best performance. Additionally, we find that preference derived from similar users is important for in-town users while social influence becomes more important for out-of-town users.

133 citations

Journal ArticleDOI
TL;DR: In this article, the authors discuss the generation, detection, and long-haul transmission of single-polarization differential quadrature phase shift keying (DQPSK) signals at a line rate of 53.5 Gbaud to support a net information bit rate of 100 Gb/s.
Abstract: We discuss the generation, detection, and long-haul transmission of single-polarization differential quadrature phase shift keying (DQPSK) signals at a line rate of 53.5 Gbaud to support a net information bit rate of 100 Gb/s. In the laboratory, we demonstrate 10-channel wavelength-division multiplexed (WDM) point-to-point transmission over 2000 km on a 150-GHz WDM grid, and 1200-km optically routed networking including 6 reconfigurable optical add/drop multiplexers (ROADMs) on a 100-GHz grid. We then report transmission over the commercial, 50-GHz spaced long-haul optical transport platform LambdaXtremereg. In a straight-line laboratory testbed, we demonstrate single-channel 700-km transmission, including an intermediate ROADM. On a field-deployed, live traffic bearing Verizon installation between Tampa and Miami, Florida, we achieve 500-km transmission, with no changes to the commercial system hardware or software and with 6 dB system margin. On the same operational system, we finally demonstrate 100-Gb/s DQPSK encoding on a field-programmable gate array (FPGA) and the transmission of real-time video traffic.

130 citations

Journal ArticleDOI
TL;DR: This article proposes a novel mining-based location prediction approach called Geographic-Temporal-Semantic-based Location Prediction (GTS-LP), which takes into account a user's geographic-triggered intentions, temporal-trigious intentions, and semantic-tracked intentions, to estimate the probability of the user in visiting a location.
Abstract: In recent years, research on location predictions by mining trajectories of users has attracted a lot of attention. Existing studies on this topic mostly treat such predictions as just a type of location recommendation, that is, they predict the next location of a user using location recommenders. However, an user usually visits somewhere for reasons other than interestingness. In this article, we propose a novel mining-based location prediction approach called Geographic-Temporal-Semantic-based Location Prediction (GTS-LP), which takes into account a user's geographic-triggered intentions, temporal-triggered intentions, and semantic-triggered intentions, to estimate the probability of the user in visiting a location. The core idea underlying our proposal is the discovery of trajectory patterns of users, namely GTS patterns, to capture frequent movements triggered by the three kinds of intentions. To achieve this goal, we define a new trajectory pattern to capture the key properties of the behaviors that are motivated by the three kinds of intentions from trajectories of users. In our GTS-LP approach, we propose a series of novel matching strategies to calculate the similarity between the current movement of a user and discovered GTS patterns based on various moving intentions. On the basis of similitude, we make an online prediction as to the location the user intends to visit. To the best of our knowledge, this is the first work on location prediction based on trajectory pattern mining that explores the geographic, temporal, and semantic properties simultaneously. By means of a comprehensive evaluation using various real trajectory datasets, we show that our proposed GTS-LP approach delivers excellent performance and significantly outperforms existing state-of-the-art location prediction methods.

128 citations

Proceedings ArticleDOI
29 Oct 2012
TL;DR: This paper analyzes the decision making process in a group to propose a personal impact topic (PIT) model for group recommendations, which effectively identifies the group preference profile for a given group by considering the personal preferences and personal impacts of group members.
Abstract: Group activities are essential ingredients of people's social life. The rapid growth of online social networking services has greatly boosted group activities by providing convenient platform for users to organize and participate in such activities. Therefore, recommender systems, as a critical component in social networking services, now face new challenges in supporting group activities. In this paper, we study the group recommendation problem, i.e., making recommendations to a group of people in social networking services. We analyze the decision making process in a group to propose a personal impact topic (PIT) model for group recommendations. The PIT model effectively identifies the group preference profile for a given group by considering the personal preferences and personal impacts of group members. Moreover, we further enhance the discovery of personal impact with social network information to obtain an extended personal impact topic (E-PIT) model. We have conducted comprehensive data analysis and evaluations on three real datasets. The results show that our proposed group recommendation techniques outperform baseline approaches.

127 citations

Journal ArticleDOI
TL;DR: It is suggested that techniques using signatures are suitable for realtime information filtering on mobile clients, and it is shown that the multi-level signature method is in general better than the other two methods.
Abstract: This paper discusses the issue of power conservation on mobile clients, e.g., palmtop, in wireless and mobile environments. It suggests that techniques using signatures are suitable for realtime information filtering on mobile clients. Three signature-based approaches, namely simple signature, integrated signature and multi-level signature schemes, are presented. The cost models for the access time and tune-in time of these three approaches are developed. We show that the multi-level signature method is in general better than the other two methods.

124 citations


Cited by
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01 Jan 2002

9,314 citations

Journal ArticleDOI

6,278 citations

Proceedings ArticleDOI
21 Aug 2011
TL;DR: A model of human mobility that combines periodic short range movements with travel due to the social network structure is developed and it is shown that this model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance.
Abstract: Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.

2,922 citations

01 Nov 2008

2,686 citations

Journal ArticleDOI
TL;DR: This review presents the emergent field of temporal networks, and discusses methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems.
Abstract: A great variety of systems in nature, society and technology -- from the web of sexual contacts to the Internet, from the nervous system to power grids -- can be modeled as graphs of vertices coupled by edges The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems In many cases, however, the edges are not continuously active As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts In some cases, edges are active for non-negligible periods of time: eg, the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks

2,452 citations