You are here: Home Teaching Summer 2014 Seminar Web Science Topics, References and Assignment
Document Actions

Topics, References and Assignment

First Session: Building User Profiles (July 8th):

  1. User profiles in online shopping
    Web retailing adoption: exploring the nature of internet users Web retailing behaviour Aron O'Cass, Tino Fenech http://www.sciencedirect.com/science/article/pii/S0969698902000048
  2. User profile  building by keyword and tag extraction
    User Profiling Based on Keyword clusters for improved recommendations: http://link.springer.com/chapter/10.1007/978-3-319-04483-5_19
    Integrating tags in semantic content-based recommender http://dl.acm.org/citation.cfm?doid=1454008.1454036
     Exploiting Big Data for Enhanced Representations in Content-Based Recommender Systems: http://link.springer.com/chapter/10.1007/978-3-642-39878-0_1
  3. User profile building using profile features
    A Machine Learning Approach to Twitter User Classification http://coitweb.uncc.edu/~anraja/courses/SMS/SMSBib/2886-14198-1-PB.pdf
    A picture is worth a thousand words: A content analysis of Facebook profile photographs http://www.sciencedirect.com/science/article/pii/S0747563211000690
    Private traits and attributes are predictable from digital records of human behavior http://www.pnas.org/content/110/15/5802.short
    Towards social user profiling: unified and discriminative influence model for inferring home locations http://dl.acm.org/citation.cfm?id=2339692
  4. User profile building using connections
    Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors http://www.networkdynamics.org/static/publication_files/ZamalLiuRuths_ICWSM2012.pdf
    Show me your friends and I will tell you what type of person you are: How one's profile, number of friends, and type of friends influence impression formation on social network sites http://onlinelibrary.wiley.com/store/10.1111/j.1083-6101.2010.01522.x/asset/j.1083-
    Same Places, Same Things, Same People? Mining User Similarity on Social Media http://citeseerx.ist.psu.edu/viewdoc/download?doi=
  5. User profile building using interactions
    Identifying Communicator Roles in Twitter http://eprints.soton.ac.uk/335268/1/wk10p14-tinati.pdf
    User profiling on Twitter http://www.semantic-web-journal.net/sites/default/files/swj198.pdf
  6. Risks of online user profiles
    Abusing Social Networks for Automated User Profiling Marco Balduzzi, Christian Platzer, Thorsten Holz, Engin Kirda, Davide Balzarotti, Christopher Kruegel http://link.springer.com/chapter/10.1007/978-3-642-15512-3_22


Second Session: Using Profiles (July 15th):

  1. Classical Recommendation Systems
    Overview on recommendation approaches:
    Recommender Systems survey: http://www.sciencedirect.com/science/article/pii/S0950705113001044#
    Recommendation use case:
    Social knowledge-based recommender system. Application to the movies domain:http://www.sciencedirect.com/science/article/pii/S0957417412004952#
    Facing the cold start problem in recommender systems http://www.sciencedirect.com/science/article/pii/S0957417413007240

  2. Recommendations in online media
    Combining Social Music and Semantic Web for music-related recommender systems: http://ceur-ws.org/Vol-405/paper3.pdf
    Evaluating Hybrid Music Recommender Systems http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6689994&tag=1
    A Cascade-Hybrid Music Recommender System for mobile services based on musical genre classification and personality diagnosis http://link.springer.com/article/10.1007/s11042-011-0742-0
    Social Context-Based Movie Recommendation: A Case Study on MyMovieHistory http://link.springer.com/chapter/10.1007/978-3-319-05939-6_33

  3. Privacy Risks of Recommendations
    "You Might Also Like:" Privacy Risks of Collaborative Filtering  Joseph A. Calandrino, Ann Kilzer, Arvind Narayanan, Edward Felten, and Vitaly Shmatikov http://dx.doi.org/10.1109/SP.2011.40
  4. Smart Home Profiling
    Profile-based control for central domestic hot water distribution F. Iglesias Vazquez and  P. Palensky, http://www.ibpsa.org/proceedings/BS2013/p_1032.pdf
    Home Energy Saving through a User Profiling System based on Wireless Sensors Antimo Barbato, Luca Borsani, Antonio Capone, Stefano Melzi  http://www.ict-aim.eu/fileadmin/user_files/Builsys09.pdf
    Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor  A.G. Ruzzelli, C. Nicolas, A. Schoofs, and G.M.P. O'Hare http://dx.doi.org/10.1109/SECON.2010.5508244
  5. Profiles and Pricing
    Detecting price and search discrimination on the internet  Jakub Mikians, László Gyarmati, Vijay Erramilli, Nikolaos Laoutaris  http://dl.acm.org/citation.cfm?id=2390245
  6. Profile Mining and Privacy
    Normality Mining: Privacy Implications of Behavioral Profiles Drawn From   GPS Enabled Mobile Phones Mark N. Gasson, Eleni Kosta, Denis Royer, Martin Meints, and Kevin Warwick http://dx.doi.org/10.1109/TSMCC.2010.2071381
    Transparent Accountable Data Mining: New Strategies for Privacy  Protection   Weitzner, Abelson, Berners-Lee, Hanson, Hendler, Kagal, McGuinness,   Sussman, Waterman   http://dig.csail.mit.edu/2006/01/tami-privacy-strategies-aaai.pdf

« March 2018 »
Personal tools