SRS2014

  • SRS2014 (5th International Workshop on Social Recommender Systems)
  • Tuesday, 8th, 09:00~17:30, Room 308 A
  • Presenters:
    Keynote Speaker: Michelle X Zhou (IBM Research)
     System U: Computational Discovery of Personality Traits from Social Media to Deliver Hyper-Personalized Experience
    Colin Cooper, Sang Hyuk Lee, Tomasz Radzik and Yiannis Siantos
     Random Walks in Recommender Systems: Exact Computation and Simulations
    Jong-Ryul Lee and Chin-Wan Chung
     A New Correlation-based Information Diffusion Prediction
    Noor Ifada and Richi Nayak
     Tensor-based Item Recommendation using Probabilistic Ranking in Social Tagging Systems
    Keynote Speaker: Irwin King (The Chinese University of Hong Kong)
     Social and Location-based Recommendations
    Lionel Martin, Valentina Sintsova and Pearl Pu
     Are Influential Writers More Objective? An Analysis of Emotionality in Review Comments
    Bin Yin, Yujiu Yang and Wenhuang Liu
     Exploring Social Activeness and Dynamic Interest in Community-based Recommender System
    Skanda Vasudevan and Sutanu Chakraborti
     Mining User Trails in Critiquing Based Recommenders
    Emanuel Lacic, Dominik Kowald, Denis Parra and Christoph Trattner
     Towards a Scalable Social Recommender Engine for Online Marketplaces: The Case of Apache Solr
    Feng Xia, Nana Yaw Asabere, Haifeng Liu, Nakema Deonauth and Fengqi Li
     Folksonomy Based Socially-Aware Recommendation of Scholarly Papers for Conference Participants
    Kun Tu, Bruno Ribeiro, Hua Jiang, Xiaodong Wang, David Jensen, Benyuan Liu and Don Towsley.
     Online Dating Recommendations: Matching Markets and Learning Preferences
  • Program:
    09:00-09:30 – Opening and Introduction

    09:30-10:30 – Industry Keynote
    System U: Computational Discovery of Personality Traits from Social Media to Deliver Hyper-Personalized Experience
    Michelle X Zhou (IBM Research – Almaden)

    10:30-11:00 – Coffee Break

    11:00-12:00 – Paper Session I (Algorithm of social recommender systems)

    Random Walks in Recommender Systems: Exact Computation and Simulations
    Colin Cooper, Sang Hyuk Lee, Tomasz Radzik and Yiannis Siantos.

    A New Correlation-based Information Diffusion Prediction
    Jong-Ryul Lee and Chin-Wan Chung.

    Tensor-based Item Recommendation using Probabilistic Ranking in Social Tagging Systems
    Noor Ifada and Richi Nayak.

    12:00-13:30 – Lunch

    13:30-14:30 – Research Keynote
    Social and Location-based Recommendations
    Irwin King (Chinese University of Hong Kong)

    14:30-15:30 – Paper Session II (User modeling of social recommender systems)
    Are Influential Writers More Objective? An Analysis of Emotionality in Review Comments
    Lionel Martin, Valentina Sintsova and Pearl Pu.

    Exploring Social Activeness and Dynamic Interest in Community-based Recommend System
    Bin Yin, Yujiu Yang and Wenhuang Liu.

    Mining User Trails in Critiquing Based Recommenders
    Skanda Raj and Sutanu Chakraborti.

    15:30-16:00 – Coffee Break

    16:00-17:30 – Paper Session III (Application of social recommender systems)
    Towards a Scalable Social Recommender Engine for Online Marketplaces: The Case of Apache Solr
    Emanuel Lacic, Dominik Kowald, Denis Parra and Christoph Trattner.

    Folksonomy Based Socially-Aware Recommendation of Scholarly Papers for Conference Participants
    Feng Xia, Nana Yaw Asabere, Haifeng Liu, Nakema Deonauth and Fengqi Li.

    Online Dating Recommendations: Matching Markets and Learned Preferences
    Kun Tu, Bruno Ribeiro, Hua Jiang, Xiaodong Wang, David Jensen, Benyuan Liu and Don Towsley.

    17:00-17:30 – Summary and Wrap up

This article has 1 comment

  1. Do you know that:

    big data dating IS NOT the key to long-lasting romance?
    and
    Personality Based Recommender Systems are the next generation of recommender systems because they perform far better than Behavioural ones (past actions and pattern of personal preferences) ?

    That is the only way to improve recommender systems, to include the personality traits of their users. They need to calculate personality similarity between users but there are different formulas to calculate similarity. Recommender systems are morphing to ………. compatibility matching engines, as the same used in the Online Dating Industry since years, with low success rates until now because they mostly use the Big Five to assess personality and the Pearson correlation coefficient to calculate similarity.
    The Big Five (Big 5, FFI, FFM, OCEAN model) normative personality test is obsolete. The HEXACO (a.k.a. Big Six) is another oversimplification. Online Dating sites have very big databases, in the range of 20,000,000 (twenty million) profiles, so the Big Five model or the HEXACO model are not enough for predictive purposes. That is why I suggest the 16PF5 test instead and another method to calculate similarity. I calculate similarity in personality patterns with (a proprietary) pattern recognition by correlation method. It takes into account the score and the trend to score of any pattern. Also it takes into account women under hormonal treatment because several studies showed contraceptive pills users make different mate choices, on average, compared to non-users. “Only short-term but not long-term partner preferences tend to vary with the menstrual cycle”.
    [Also some Psychologists began to encourage the use of other tests for the Online Dating Industry, like:
    California Psychological Inventory (CPI)
    and
    The Millon Index of Personality Styles-Revised (MIPS Revised)
    but my best recommendation is, of course, the 16PF5 normative personality test.
    Nor the CPI nor the MIPS can outperform the 16PF5.]

    If you want to be first in the “personalization arena” == Personality Based Recommender Systems, you should understand HOW TO INNOVATE in the ………… Online Dating Industry first of all!

    Regards,
    Fernando Ardenghi.
    Buenos Aires.
    Argentina.
    ardenghier AT gmail DOT com

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