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Wang-Chien Lee
Researcher at Pennsylvania State University
Publications - 367
Citations - 15328
Wang-Chien Lee is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Wireless sensor network & Mobile computing. 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.
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Proceedings ArticleDOI
Exploiting geographical influence for collaborative point-of-interest recommendation
TL;DR: This paper argues that the geographical influence among POIs plays an important role in user check-in behaviors and model it by power law distribution, and develops a collaborative recommendation algorithm based on geographical influence based on naive Bayesian.
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HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning
TL;DR: Empirical results show that HIN2Vec soundly outperforms the state-of-the-art representation learning models for network data, including DeepWalk, LINE, node2vec, PTE, HINE and ESim, by 6.6% to 23.8% of $micro$-$f_1$ in multi-label node classification and 5% to 70.8%, in link prediction.
Proceedings ArticleDOI
Location recommendation for location-based social networks
TL;DR: A friend-based collaborative filtering approach for location recommendation based on collaborative ratings of places made by social friends is developed, and a variant of FCF technique, namely Geo-Measured FCF (GM-FCF), based on heuristics derived from observed geospatial characteristics in the Foursquare dataset is proposed.
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Prediction-based strategies for energy saving in object tracking sensor networks
TL;DR: This paper proposes a prediction-based energy saving scheme, called PES, to reduce the energy consumption for object tracking under acceptable conditions, and compares PES against the basic schemes proposed in the paper to explore the conditions under which PES is most desired.
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Event-based social networks: linking the online and offline social worlds
TL;DR: This paper is the first research to study EBSNs at scale and paves the way for future studies on this new type of social network.