Exploiting user feedback for online filtering in event-based systems

Modern large-scale internet applications, like the ubiquitous social networks, represent today a fundamental source of information for millions of users. The larger is the user base, the more difficult it is to control the quality of data that is spread from producers to consumers. This can easily hamper the usability of such systems as the amount of low quality data received by consumers grows uncontrolled. In this paper we propose a novel solution to automatically filter new data injected in event-based systems with the aim of delivering to consumers only content they are actually interested in. Filtering is executed at run-time by first profiling both producers and consumers, and then matching their profiles as new data is produced.

[1]  Cristina Nita-Rotaru,et al.  A survey of attack and defense techniques for reputation systems , 2009, CSUR.

[2]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[3]  Daniel Stutzbach,et al.  Understanding churn in peer-to-peer networks , 2006, IMC '06.

[4]  Ben Y. Zhao,et al.  An Empirical Study of Collusion Behavior in the Maze P2P File-Sharing System , 2007, 27th International Conference on Distributed Computing Systems (ICDCS '07).

[5]  Luca de Alfaro,et al.  Reputation systems for open collaboration , 2011, Commun. ACM.

[6]  David Eyers,et al.  Living in the present: on-the-fly information processing in scalable web architectures , 2012, CloudCP '12.

[7]  Donald F. Towsley,et al.  Channelization problem in large scale data dissemination , 2001, Proceedings Ninth International Conference on Network Protocols. ICNP 2001.

[8]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[10]  Audun Jøsang,et al.  AIS Electronic Library (AISeL) , 2017 .

[11]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

[12]  Chrysanthos Dellarocas.  Designing Reputation Systems for the Social Web , 2010 .

[13]  Stratis Ioannidis,et al.  Surfing the Blogosphere: Optimal Personalized Strategies for Searching the Web , 2010, 2010 Proceedings IEEE INFOCOM.

[14]  David M. Kreps,et al.  Reputation and imperfect information , 1982 .

[15]  Roberto Beraldi,et al.  LCBM: Statistics-Based Parallel Collaborative Filtering , 2014, BIS.

[16]  Chrysanthos Dellarocas,et al.  Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior , 2000, EC '00.

[17]  David S. Rosenblum,et al.  Design and evaluation of a wide-area event notification service , 2001, TOCS.

[18]  Roberto Beraldi,et al.  Brief Announcement: Distributed Self-organizing Event Space Partitioning for Content-Based Publish/Subscribe Systems , 2011, SSS.

[19]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[20]  Sasu Tarkoma,et al.  Distributed event routing in publish/subscribe communication systems , 2009 .

[21]  Audun Jøsang,et al.  A survey of trust and reputation systems for online service provision , 2007, Decis. Support Syst..

[22]  Hector Garcia-Molina,et al.  The Eigentrust algorithm for reputation management in P2P networks , 2003, WWW '03.

[23]  Barry Smyth,et al.  Towards the Profiling of Twitter Users for Topic-Based Filtering , 2012, SGAI Conf..

[24]  Mario Paolucci.  False reputation in social control , 2000, Adv. Complex Syst..

[25]  Sasu Tarkoma,et al.  Distributed Event Routing in Publish/Subscribe Systems , 2009, Middleware for Network Eccentric and Mobile Applications.

[26]  Mario Paolucci,et al.  Electronic reputation systems , 2009 .