Strategies for Mining User Preferences in a Data Stream Setting

In this article, we formally introduce the problem of mining contextual preferences in a data stream setting. Contextual Preferences have been recently treated in the literature  and some methods for mining this special kind of preference have been proposed in the batch setting. Besides the formalization of the contextual preference mining problem in the stream setting, we propose two strategies for solving this problem. In order to evaluate our proposals, we implemented one of these strategies, the greedy one, and we compared its performance with a well known baseline in the literature, showing its efficiency through a set of experiments over real data.

[1]  Geoff Hulten,et al.  Mining high-speed data streams , 2000, KDD '00.

[2]  Albert Bifet,et al.  Massive Online Analysis , 2009 .

[3]  Nic Wilson,et al.  Extending CP-Nets with Stronger Conditional Preference Statements , 2004, AAAI.

[4]  Jesús S. Aguilar-Ruiz,et al.  Knowledge discovery from data streams , 2009, Intell. Data Anal..

[5]  Stuart J. Russell,et al.  Online bagging and boosting , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[6]  Albert Bifet,et al.  DATA STREAM MINING A Practical Approach , 2009 .

[7]  Shangce Gao,et al.  Bio-Inspired Computational Algorithms and Their Applications , 2012 .

[8]  Werner Kießling,et al.  Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications , 2003, PKDD.

[9]  Geoff Holmes,et al.  MOA: Massive Online Analysis , 2010, J. Mach. Learn. Res..

[10]  Sandra de Amo,et al.  CPrefMiner: An Algorithm for Mining User Contextual Preferences Based on Bayesian Networks , 2012, 2012 IEEE 24th International Conference on Tools with Artificial Intelligence.

[11]  Raju Nedunchezhian,et al.  Mining data streams with concept drifts using genetic algorithm , 2011, Artificial Intelligence Review.

[12]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[13]  Koby Crammer,et al.  Pranking with Ranking , 2001, NIPS.

[14]  Bin Jiang,et al.  Mining preferences from superior and inferior examples , 2008, KDD.

[15]  Chen Wu,et al.  Mining Context-based User Preferences for m-Services Applications , 2007, IEEE/WIC/ACM International Conference on Web Intelligence (WI'07).

[16]  Din J. Wasem,et al.  Mining of Massive Datasets , 2014 .

[17]  Thorsten Joachims,et al.  Online Learning with Preference Feedback , 2011, ArXiv.

[18]  Han La Poutré,et al.  A Fast Method for Learning Non-linear Preferences Online Using Anonymous Negotiation Data , 2006, TADA/AMEC.

[19]  Vipin Kumar,et al.  Introduction to Data Mining, (First Edition) , 2005 .