Traveler's Next Activity Predication with Location-Based Social Network Data

The rise of technology and the internet provides powerful means for people from all around the world to communicate and connect with one another. Online social network platforms become go-to places for users to express and share their individuality, which includes choice of activities, locations and associated timestamps. In turn, their opinions affect the point of view of others, who are in their online friendship circle. Users' increasing usage of social networks help accumulate massive amount of data that can be further explored. Particularly, this type of data attracts and allows researchers, who are interested in studying and understanding how social factors and previous experience influence user behavior in term of activity-related travel choice. In this paper, the goal is to utilize such rich data sources to build a model that predicts user next activity. Such model contributes a powerful tool for integrating the location prediction with transportation planning and operations process. Besides, it is valuable in commercial applications to create better recommendation system with higher accuracy and ultimately attract more customers to partnering businesses. By studying the dataset, which contains millions of historical check-ins from thousands of users, it is possible to derive information that are useful in predicting user next activity. The proposed approach applies machine learning techniques on the collected features to deliver highly accurate prediction results with fast training and prediction time.

[1]  Xing Xie,et al.  Collaborative activity recognition via check-in history , 2011, LBSN '11.

[2]  A. Izenman.  Linear Discriminant Analysis , 2013 .

[3]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[4]  Zhe Zhu,et al.  What's Your Next Move: User Activity Prediction in Location-based Social Networks , 2013, SDM.

[5]  Shuai Xu,et al.  Effective fine-grained location prediction based on user check-in pattern in LBSNs , 2018, J. Netw. Comput. Appl..

[6]  Cecilia Mascolo,et al.  Exploiting Foursquare and Cellular Data to Infer User Activity in Urban Environments , 2013, 2013 IEEE 14th International Conference on Mobile Data Management.

[7]  김은이,et al.  Mean Shift Clustering을 이용한 영상 검색결과 개선 , 2009 .

[8]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[9]  Eric Sun,et al.  Location3: How Users Share and Respond to Location-Based Data on Social , 2011, ICWSM.

[10]  Fabio Pianese,et al.  Discovering and predicting user routines by differential analysis of social network traces , 2013, 2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).

[11]  Gilles Louppe,et al.  Understanding Random Forests , 2015 .

[12]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[13]  Robert H. Riffenburgh,et al.  Linear Discriminant Analysis , 1960 .

[14]  Song Guo,et al.  Crowdsourcing-Based Content-Centric Network: A Social Perspective , 2017, IEEE Network.

[15]  Velin Kounev,et al.  Where will I go next?: Predicting future categorical check-ins in Location Based Social Networks , 2012, 8th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom).

[17]  Nizar Bouguila,et al.  Online Learning of Hierarchical Pitman–Yor Process Mixture of Generalized Dirichlet Distributions With Feature Selection , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[18]  J. Pitman,et al.  The two-parameter Poisson-Dirichlet distribution derived from a stable subordinator , 1997 .

[19]  Harry Zhang,et al.  The Optimality of Naive Bayes , 2004, FLAIRS.

[20]  Leif E. Peterson.  K-nearest neighbor , 2009, Scholarpedia.

[21]  Huan Liu,et al.  Exploring Social-Historical Ties on Location-Based Social Networks , 2012, ICWSM.

[22]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[23]  Wen Li,et al.  Want a coffee?: predicting users' trails , 2012, SIGIR '12.

[24]  Cecilia Mascolo,et al.  Mining User Mobility Features for Next Place Prediction in Location-Based Services , 2012, 2012 IEEE 12th International Conference on Data Mining.

[25]  Hani S. Mahmassani,et al.  Exploring Activity and Destination Choice Behavior in Social Networking Data , 2015 .

[26]  Sameep Mehta,et al.  Inferring and Exploiting Categories for Next Location Prediction , 2015, WWW.

[27]  Author.  $article.title , 2002, Nature.

[28]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[29]  Daqing Zhang,et al.  Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.