Managing Natural Noise in Recommender Systems

E-commerce customers demand quick and easy access to suitable products in large purchase spaces. To support and facilitate this purchasing process to users, recommender systems (RSs) help them to find out the information that best fits their preferences and needs in an overloaded search space. These systems require the elicitation of customers’ preferences. However, this elicitation process is not always precise either correct because of external factors such as human errors, uncertainty, human beings inherent inconsistency and so on. Such a problem in RSs is known as natural noise (NN) and can negatively bias recommendations, which leads to poor user’s experience. Different proposals have been presented to deal with natural noise in RSs. Several of them require additional interaction with customers. Others just remove noisy information. Recently, new NN approaches dealing with the ratings stored in the user/item rating matrix have raised to deal with NN in a better and simpler way. This contribution is devoted to provide a brief review of the latter approaches revising crisp and fuzzy approaches for dealing with NN in RSs. Eventually it points out as a future research the management of NN in other recommendation scenarios as group RSs.

[1]  Macarena Espinilla,et al.  Using linguistic incomplete preference relations to cold start recommendations , 2010, Internet Res..

[2]  ManolopoulosYannis,et al.  Collaborative recommender systems , 2008 .

[3]  Xavier Amatriain,et al.  The wisdom of the few: a collaborative filtering approach based on expert opinions from the web , 2009, SIGIR.

[4]  Luis Martínez-López,et al.  A mobile 3D-GIS hybrid recommender system for tourism , 2012, Inf. Sci..

[5]  Luis Martínez,et al.  A multigranular linguistic content-based recommendation model: Research Articles , 2007 .

[6]  Neil J. Hurley,et al.  Detecting noise in recommender system databases , 2006, IUI '06.

[7]  John Riedl,et al.  Recommender systems: from algorithms to user experience , 2012, User Modeling and User-Adapted Interaction.

[8]  Yailé Caballero Mota,et al.  An e-Learning Collaborative Filtering Approach to Suggest Problems to Solve in Programming Online Judges , 2014, Int. J. Distance Educ. Technol..

[9]  Luis Martínez-López,et al.  A fuzzy model for managing natural noise in recommender systems , 2016, Appl. Soft Comput..

[10]  Nuria Oliver,et al.  I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems , 2009, UMAP.

[11]  Luis Martínez-López,et al.  A multigranular linguistic content‐based recommendation model , 2007, Int. J. Intell. Syst..

[12]  Macarena Espinilla,et al.  A Knowledge Based Recommender System with Multigranular Linguistic Information , 2007, Int. J. Comput. Intell. Syst..

[13]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[14]  Konstantinos G. Margaritis,et al.  Using SVD and demographic data for the enhancement of generalized Collaborative Filtering , 2007, Inf. Sci..

[15]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[16]  Domonkos Tikk,et al.  Recommending new movies: even a few ratings are more valuable than metadata , 2009, RecSys '09.

[17]  Huseyin Polat,et al.  Shilling attacks against recommender systems: a comprehensive survey , 2014, Artificial Intelligence Review.

[18]  Luis Martínez-López,et al.  Managing experts behavior in large-scale consensus reaching processes with uninorm aggregation operators , 2015, Appl. Soft Comput..

[19]  Nuria Oliver,et al.  Data Mining Methods for Recommender Systems , 2015, Recommender Systems Handbook.

[20]  Izak Benbasat,et al.  E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact , 2007, MIS Q..

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

[22]  Guy Shani,et al.  A Survey of Accuracy Evaluation Metrics of Recommendation Tasks , 2009, J. Mach. Learn. Res..

[23]  John Riedl,et al.  Collaborative Filtering Recommender Systems , 2011, Found. Trends Hum. Comput. Interact..

[24]  Rosa M. Rodríguez,et al.  Weighting of Features in Content-Based Filtering with Entropy and Dependence Measures , 2014, Int. J. Comput. Intell. Syst..

[25]  Xavier Amatriain,et al.  Data Mining Methods for Recommender Systems , 2011, Recommender Systems Handbook.

[26]  Chengqi Zhang,et al.  Noisy but non-malicious user detection in social recommender systems , 2012, World Wide Web.

[27]  Bamshad Mobasher,et al.  Towards Trustworthy Recommender Systems : An Analysis of Attack Models and Algorithm Robustness , 2007 .

[28]  Luis Martínez-López,et al.  Correcting noisy ratings in collaborative recommender systems , 2015, Knowl. Based Syst..

[29]  George Karypis,et al.  A Comprehensive Survey of Neighborhood-based Recommendation Methods , 2011, Recommender Systems Handbook.

[30]  Francisco Herrera,et al.  An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges , 2012, Inf. Sci..

[31]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[32]  Luis Martínez-López,et al.  An Overview on Fuzzy Modelling of Complex Linguistic Preferences in Decision Making , 2016, Int. J. Comput. Intell. Syst..

[33]  Javier Montero,et al.  Computational intelligence in decision making , 2014, Int. J. Comput. Intell. Syst..