A Probability-Based Hybrid User Model for Recommendation System

With the rapid development of information communication technology, the available information or knowledge is exponentially increased, and this causes the well-known information overload phenomenon. This problem is more serious in product design corporations because over half of the valuable design time is consumed in knowledge acquisition, which highly extends the design cycle and weakens the competitiveness. Therefore, the recommender systems become very important in the domain of product domain. This research presents a probability-based hybrid user model, which is a combination of collaborative filtering and content-based filtering. This hybrid model utilizes user ratings and item topics or classes, which are available in the domain of product design, to predict the knowledge requirement. The comprehensive analysis of the experimental results shows that the proposed method gains better performance in most of the parameter settings. This work contributes a probability-based method to the community for implement recommender system when only user ratings and item topics are available.

[1]  Wei Wang,et al.  Recommender system application developments: A survey , 2015, Decis. Support Syst..

[2]  Mansoor Zolghadri Jahromi,et al.  USING CONTENT FEATURES TO ENHANCE THE PERFORMANCE OF USER -BASED COLLABORATIVE FILTERING , 2014 .

[3]  Mohd Abdul Hameed,et al.  Collaborative Filtering Based Recommendation System: A survey , 2012 .

[4]  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.

[5]  Jia Hao,et al.  A User-Oriented Design Knowledge Reuse Model , 2013 .

[6]  Robin D. Burke,et al.  Hybrid Web Recommender Systems , 2007, The Adaptive Web.

[7]  Stuart E. Middleton,et al.  Ontology-based Recommender Systems , 2004, Handbook on Ontologies.

[8]  Chien Chin Chen,et al.  An effective recommendation method for cold start new users using trust and distrust networks , 2013, Inf. Sci..

[9]  Kyoung-Yun Kim,et al.  DCR-based causal design knowledge evaluation method and system for future CAD applications , 2012, Comput. Aided Des..

[10]  A. A. Ammar,et al.  KNOWLEDGE REUSE : TOWARDS A DESIGN TOOL , 2010 .

[11]  Juan C. Burguillo,et al.  A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition , 2010, Inf. Sci..

[12]  Pasquale Lops,et al.  A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation , 2007, User Modeling and User-Adapted Interaction.

[13]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[14]  Antonio Moreno,et al.  SigTur/E-Destination: Ontology-based personalized recommendation of Tourism and Leisure Activities , 2013, Eng. Appl. Artif. Intell..

[15]  Sonia Bergamaschi,et al.  Guest Editors' Introduction: Information Overload , 2010, IEEE Internet Computing.

[16]  Lina Yao,et al.  Recommending Web Services via Combining Collaborative Filtering with Content-Based Features , 2013, 2013 IEEE 20th International Conference on Web Services.

[17]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[18]  Gary Wills,et al.  Knowledge use in an advanced manufacturing environment , 2011 .

[19]  Tsvi Kuflik,et al.  Cross-representation mediation of user models , 2009, User Modeling and User-Adapted Interaction.

[20]  Royi Ronen,et al.  Selecting content-based features for collaborative filtering recommenders , 2013, RecSys.

[21]  Tsvi Kuflik,et al.  Mediation of user models for enhanced personalization in recommender systems , 2007, User Modeling and User-Adapted Interaction.

[22]  K. Margaritis,et al.  Analysis of Recommender Systems’ Algorithms , 2003 .

[23]  Uday V. Kulkarni,et al.  Hybrid personalized recommender system using centering-bunching based clustering algorithm , 2012, Expert Syst. Appl..

[24]  J. H. Ge,et al.  Research on Method of Product Configuration Design Based on Product Family Ontology Model , 2013 .

[25]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[26]  Kee-Sung Lee,et al.  Collaborative user modeling for enhanced content filtering in recommender systems , 2011, Decis. Support Syst..

[27]  Stephen Wan,et al.  Supporting browsing-specific information needs: Introducing the Citation-Sensitive In-Browser Summariser , 2010, J. Web Semant..

[28]  Robin Burke,et al.  Knowledge-based recommender systems , 2000 .

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

[30]  Marijn Koolen,et al.  Workshop on new trends in content-based recommender systems: (CBRecSys 2014) , 2014, RecSys '14.

[31]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[32]  Kamal Kant Bharadwaj,et al.  Fuzzy-genetic approach to recommender systems based on a novel hybrid user model , 2008, Expert Syst. Appl..