Lattice navigation for collaborative filtering by means of (fuzzy) formal concept analysis

Recommender systems rely on the opinions of a community of users to provide "recommendations" that can help users of the same community in discerning content of interest from a wide range of possibilities. Particularly, collaborative information filtering represents one of techniques widely exploited by recommender systems to suggest which items better meet the user needs and preferences. This paper introduces a model for collaborative filtering based on Formal Concept Analysis, a theoretical framework suitable to generate correlations among data through a lattice design. In particular, a fuzzy annotation of the lattice allows discovering similarities among items as well as users, arranged as a ranked list.

[1]  Paolo Avesani,et al.  Trust-aware recommender systems , 2007, RecSys '07.

[2]  Silvia N. Schiaffino,et al.  Entertainment recommender systems for group of users , 2011, Expert Syst. Appl..

[3]  GeunSik Jo,et al.  Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation , 2010, Electron. Commer. Res. Appl..

[4]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .

[5]  Amir Albadvi,et al.  A hybrid recommendation technique based on product category attributes , 2009, Expert Syst. Appl..

[6]  Radim Bělohlávek,et al.  Lattices of Fixed Points of Fuzzy Galois Connections , 2001 .

[7]  Zheng Pei,et al.  New Fast Algorithm for Constructing Concept Lattice , 2007, ICCSA.

[8]  Matteo Gaeta,et al.  RSS-based e-learning recommendations exploiting fuzzy FCA for Knowledge Modeling , 2012, Appl. Soft Comput..

[9]  Tsvetanka Georgieva,et al.  Using error-correcting dependencies for collaborative filtering , 2008, Data Knowl. Eng..

[10]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[11]  Vincenzo Loia,et al.  Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis , 2012, Inf. Process. Manag..

[12]  Tomohiro Murata,et al.  A knowledge-based recommendation model utilizing Formal Concept Analysis and association , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[13]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .

[14]  Fernando Ortega,et al.  Improving collaborative filtering recommender system results and performance using genetic algorithms , 2011, Knowl. Based Syst..

[15]  John Riedl,et al.  Learning preferences of new users in recommender systems: an information theoretic approach , 2008, SKDD.

[16]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[17]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[18]  Derrick G. Kourie,et al.  AddIntent: A New Incremental Algorithm for Constructing Concept Lattices , 2004, ICFCA.

[20]  Siyao Zheng,et al.  A Research on Fuzzy Formal Concept Analysis Based Collaborative Filtering Recommendation System , 2009, 2009 Second International Symposium on Knowledge Acquisition and Modeling.

[21]  Derek G. Bridge,et al.  Collaborative Recommending using Formal Concept Analysis , 2006, Knowl. Based Syst..

[22]  Thomas Hofmann,et al.  Latent semantic models for collaborative filtering , 2004, TOIS.