A proposed configurable approach for recommendation systems via data mining techniques

ABSTRACT This study presents a configurable approach for recommendations which determines the suitable recommendation method for each field based on the characteristics of its data, the method includes determining the suitable technique for selecting a representative sample of the provided data. Then selecting the suitable feature weighting measure to provide a correct weight for each feature based on its effect on the recommendations. Finally, selecting the suitable algorithm to provide the required recommendations. The proposed configurable approach could be applied on different domains. The experiments have revealed that the approach is able to provide recommendations with only 0.89 error rate percentage.

[1]  Christoph Schlieder,et al.  A Spatial User Similarity Measure for Geographic Recommender Systems , 2009, GeoS.

[2]  Alejandro Bellogín,et al.  An empirical comparison of social, collaborative filtering, and hybrid recommenders , 2013, TIST.

[3]  Hesham Ahmed Hassan,et al.  Sampling technique selection framework for knowledge discovery , 2010, 2010 The 7th International Conference on Informatics and Systems (INFOS).

[4]  Laxman Sahoo,et al.  A Survey on Recommendation System , 2017 .

[5]  SongJie Gong,et al.  A personalized recommendation system combining case-based reasoning and user-based collaborative filtering , 2009, 2009 Chinese Control and Decision Conference.

[6]  Ram D. Gopal,et al.  Empirical Analysis of the Impact of Recommender Systems on Sales , 2010, J. Manag. Inf. Syst..

[7]  Ahmet Arslan,et al.  A collaborative filtering method based on artificial immune network , 2009, Expert Syst. Appl..

[8]  Yuan-Chun Jiang,et al.  Maximizing customer satisfaction through an online recommendation system: A novel associative classification model , 2010, Decis. Support Syst..

[9]  José Juan Pazos-Arias,et al.  Exploiting digital TV users' preferences in a tourism recommender system based on semantic reasoning , 2010, IEEE Transactions on Consumer Electronics.

[10]  Barry Smyth,et al.  A recommender system for on-line course enrolment: an initial study , 2007, RecSys '07.

[11]  Indrajit Mandal,et al.  Machine learning algorithms for the creation of clinical healthcare enterprise systems , 2016, Enterp. Inf. Syst..

[12]  Li Chen,et al.  Human Decision Making and Recommender Systems , 2015, Recommender Systems Handbook.

[13]  Dunja Mladenic,et al.  Web Mining: From Web to Semantic Web , 2004, Lecture Notes in Computer Science.

[14]  Marko Grobelnik,et al.  Feature selection using linear classifier weights: interaction with classification models , 2004, SIGIR '04.

[15]  Andrew W. H. Ip,et al.  A dynamic decision support system to predict the value of customer for new product development , 2011, Decis. Support Syst..

[16]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[17]  Ali Mamat,et al.  WebPUM: A Web-based recommendation system to predict user future movements , 2010, Expert Syst. Appl..

[18]  Yoon Ho Cho,et al.  A personalized recommender system based on web usage mining and decision tree induction , 2002, Expert Syst. Appl..

[19]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Wolfgang Woerndl,et al.  Introducing Context into Recommender Systems , 2007 .

[21]  Narimel Bendakir,et al.  Using Association Rules for Course Recommendation , 2006 .

[22]  William Nick Street,et al.  Healthcare information systems: data mining methods in the creation of a clinical recommender system , 2011, Enterp. Inf. Syst..

[23]  M. L. Valarmathi,et al.  GAIN RATIO BASED FEATURE SELECTION METHOD FOR PRIVACY PRESERVATION , 2011 .

[24]  Nitis Mukhopadhyay,et al.  Correlation Coefficient , 2011, International Encyclopedia of Statistical Science.

[25]  Xin Jin,et al.  Semantically Enhanced Collaborative Filtering on the Web , 2003, EWMF.

[26]  Li Chen,et al.  A user-centric evaluation framework for recommender systems , 2011, RecSys '11.

[27]  Abbas Badiehneshin,et al.  Data Mining and its Application in Recommender Systems based on Tensor Decomposition Method , 2015 .

[28]  Zhi-Dan Zhao,et al.  User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[29]  Alina Pommeranz,et al.  Designing Human-Centered Systems for Reflective Decision Making , 2012 .

[30]  Johan Koolwaaij,et al.  Context-Aware Recommendations in the Mobile Tourist Application COMPASS , 2004, AH.

[31]  Rainer Froese,et al.  Cube law, condition factor and weight-length relationships: history, meta-analysis and recommendations , 2006 .

[32]  Kuo Zhao,et al.  Personalized recommendation algorithm based on preference features , 2014, Tsinghua Science and Technology.

[33]  Elaine Rich,et al.  User Modeling via Stereotypes , 1998, Cogn. Sci..

[34]  Carl T. Bergstrom,et al.  A Recommendation System Based on Hierarchical Clustering of an Article-Level Citation Network , 2016, IEEE Transactions on Big Data.

[35]  David W. Willis,et al.  Relative Weight (Wr) Status and Current Use in Fisheries Assessment and Management , 2000 .

[36]  Mikael J. Persson.  Challenges of implementing decision support systems: Joining the views of consultant and customer , 2015 .