Personalized Location Recommendation on Location-based Social Networks

Huiji Gao, Jiliang Tang, and Huan Liu
Arizona State University


Tutorial Abstract

Personalized location recommendation is a special topic of recommendation. It is related to human mobile behavior in the real world regarding various contexts including spatial, temporal, social, and content. The development of this topic is subject to the availability of human mobile data. The recent rapid growth of location-based social networks has alleviated such limitation, which promotes the development of various location recommendation techniques. This tutorial offers an overview, in a data mining perspective, of personalized location recommendation on location-based social networks. It introduces basic concepts, summarizes unique LBSN characteristics and research opportunities, elaborates associated challenges, reviews state-of-the-art algorithms with illustrative examples and real-world LBSN datasets, and discusses effective evaluation methods.

Tutorial Outline [Slides]

The tutorial is planned for 1.5 hours and organized into 3 sessions with 5 segments. Its detailed outline is presented below

  1. Introduction (15 mins)
    • What is Location Recommendation
    • Why Personalized
    • Why on LBSNs
    • W4: Information Layout on LBSNs
  2. LBSN Data Properties and Mobile Patterns (20 mins)
    • Data Properties
    • Mobile Patterns
  3. Location Recommendation on LBSNs (50 mins)
    • Check-in Data Representation
    • Geographical Influence
    • Social Correlations
    • Temporal Dynamics
    • Content Indications
    • Hybrid Models
  4. Conclusions and Q/A (5 mins)
 

Presenters

Huiji Gao is a Ph.D. candidate of Computer Science and Engineering at Arizona State University. He obtained his B.S. and M.S. at Beijing University of Posts and Telecommunications in 2007 and 2010, respectively. He was awarded the 2014 ASU Graduate Education Dissertation Fellowship, the 2014 ASU President's Award for Innovation, the 3rd Place Dedicated Task 2 Next Location Prediction of Nokia Mobile Data Challenge 2012, and various Student Travel Awards and Scholarships. His research interests include social computing, crowdsourcing for disaster management system, recommender systems, and mobile data mining on location-based social networks. He has published innovative works in highly ranked journals and top conference proceedings such as DMKD, IEEE Intelligent Systems, SIGKDD, CIKM, WWW, RecSys, WSDM, ICWSM, and ICDM. He has interned in IBM Research Almaden in 2013 and LinkedIn in 2014. Updated information can be found at http://www.public.asu.edu/~hgao16.

Jiliang Tang is a senior PhD student of Computer Science and Engineering at Arizona State University. He obtained his Master degree in Computer Science and Bachelor degree in Software Engineering at Beijing Institute of Technology in 2008 and 2010, respectively. He was awarded the 2014 ASU President's Award for Innovation, Best Paper Shortlist in WSDM13, the 3rd Place Dedicated Task 2 Next Location Prediction of Nokia Mobile Data Challenge 2012, University Graduate Fellowship, and various Student Travel Awards and Scholarships. His research interests are in computing with online trust and distrust, recommendation, mining social media data, data mining and feature selection. He co-presents two tutorials in KDD2014 and WWW2014, and has published innovative works in highly ranked journals and top conference proceedings such as IEEE TKDE, ACM TKDD, DMKD, ACM SIGKDD, SIGIR, WWW, WSDM, SDM, ICDM, IJCAI, AAAI, and CIKM. He also worked as a research intern in Yahoo!Labs in 2013. Updated information can be found at http://www.public.asu.edu/~jtang20.

Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He obtained his Ph.D. in Computer Science at University of Southern California and B.Eng. in Computer Science and Electrical Engineering at Shanghai JiaoTong University. Before he joined ASU, he worked at Telecom Australia Research Labs and was on the faculty at National University of Singapore. He was recognized for excellence in teaching and research in Computer Science and Engineering at Arizona State University. His research interests are in data mining, machine learning, social computing, and artificial intelligence, investigating problems that arise in many real-world, data-intensive applications with high-dimensional data of disparate forms such as social media. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He serves on journal editorial boards and numerous conference program committees, and is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction (http://sbp.asu.edu/). He is an IEEE Fellow and an ACM Distinguished Scientist. Updated information can be found at http://www.public.asu.edu/~huanliu.