A trust-semantic fusion-based recommendation approach for e-business applications

Collaborative Filtering (CF) is the most popular recommendation technique but still suffers from data sparsity, user and item cold-start problems, resulting in poor recommendation accuracy and reduced coverage. This study incorporates additional information from the users' social trust network and the items' semantic domain knowledge to alleviate these problems. It proposes an innovative Trust-Semantic Fusion (TSF)-based recommendation approach within the CF framework. Experiments demonstrate that the TSF approach significantly outperforms existing recommendation algorithms in terms of recommendation accuracy and coverage when dealing with the above problems. A business-to-business recommender system case study validates the applicability of the TSF approach.

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

[2]  Weihui Dai,et al.  Website browsing aid: A navigation graph-based recommendation system , 2008, Decis. Support Syst..

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

[4]  Jennifer Golbeck,et al.  Generating Predictive Movie Recommendations from Trust in Social Networks , 2006, iTrust.

[5]  Yi-Cheng Ku,et al.  A semantic-expansion approach to personalized knowledge recommendation , 2008, Decis. Support Syst..

[6]  Bamshad Mobasher,et al.  Intelligent Techniques for Web Personalization , 2005, Lecture Notes in Computer Science.

[7]  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.

[8]  Lior Rokach,et al.  Recommender Systems Handbook , 2010 .

[9]  Jie Lu,et al.  A WEB‐BASED PERSONALIZED BUSINESS PARTNER RECOMMENDATION SYSTEM USING FUZZY SEMANTIC TECHNIQUES , 2013, Comput. Intell..

[10]  Ram D. Gopal,et al.  Shopbot 2.0: Integrating Recommendations and Promotions with Comparison Shopping , 2006, Decis. Support Syst..

[11]  Jie Lu,et al.  Government-to-Business Personalized e-Services Using Semantic-Enhanced Recommender System , 2011, EGOVIS.

[12]  Wei Huang,et al.  A Framework for an Ontology-based E-commerce Product Information Retrieval System , 2009, J. Comput..

[13]  Matthew O. Adigun,et al.  Building an Ontology-Based Framework for Tourism Recommendation Services , 2009, ENTER.

[14]  Michael Bieber,et al.  A clickstream-based collaborative filtering personalization model: towards a better performance , 2004, WIDM '04.

[15]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[16]  John Riedl,et al.  An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms , 2002, Information Retrieval.

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

[18]  Yoon Ho Cho,et al.  Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce , 2004, Expert Syst. Appl..

[19]  Philip Resnik,et al.  Using Information Content to Evaluate Semantic Similarity in a Taxonomy , 1995, IJCAI.

[20]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[21]  Jie Lu,et al.  A Hybrid Multi-criteria Semantic-Enhanced Collaborative Filtering Approach for Personalized Recommendations , 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[22]  Jie Lu,et al.  Integrating Multi-Criteria Collaborative Filtering and Trust filtering for personalized Recommender Systems , 2011, 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM).

[23]  Jie Lu,et al.  Intelligent e-government services with personalized recommendation techniques: Research Articles , 2007 .

[24]  Jie Lu,et al.  Intelligent e‐government services with personalized recommendation techniques , 2007, Int. J. Intell. Syst..

[25]  Lun-Ping Hung,et al.  A personalized recommendation system based on product taxonomy for one-to-one marketing online , 2005, Expert Syst. Appl..

[26]  Alexander Hars,et al.  Web Based Knowledge Infrastructures for the Sciences: An Adaptive Document , 2000, Commun. Assoc. Inf. Syst..

[27]  Juozas Bivainis.  Development of Business Partner Selection , 2006 .

[28]  Fan-Sheng Kong,et al.  Semantic-Enhanced Personalized Recommender System , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[29]  Arvind K. Tripathi,et al.  Design of a shopbot and recommender system for bundle purchases , 2006, Decis. Support Syst..

[30]  José Francisco Aldana Montes,et al.  Semantically Enhanced Recommender Systems , 2009, OTM Workshops.

[31]  Chris Cornelis,et al.  Trust and Recommendations , 2011, Recommender Systems Handbook.

[32]  Guangquan Zhang,et al.  BizSeeker: A hybrid semantic recommendation system for personalized government-to-business e-services , 2010, Internet Res..

[33]  Barry Smyth,et al.  Trust in recommender systems , 2005, IUI.

[34]  Zuhua Jiang,et al.  Recommender system based on workflow , 2009, Decis. Support Syst..

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

[36]  M. Lynne Markus,et al.  Paradigm Shifts - E-Business and Business/Systems Integration , 2000, Commun. Assoc. Inf. Syst..

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

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

[39]  Tim Weitzel,et al.  Decision support for team staffing: An automated relational recommendation approach , 2008, Decis. Support Syst..

[40]  Chein-Shung Hwang,et al.  Using Trust in Collaborative Filtering Recommendation , 2007, IEA/AIE.

[41]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[42]  Jie Lu,et al.  A hybrid trust‐enhanced collaborative filtering recommendation approach for personalized government‐to‐business e‐services , 2011, Int. J. Intell. Syst..

[43]  Abdulmotaleb El-Saddik,et al.  Collaborative error-reflected models for cold-start recommender systems , 2011, Decis. Support Syst..

[44]  Françoise Fessant,et al.  Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems , 2008, ICDM.

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

[46]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[47]  Dimitris Plexousakis,et al.  Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences , 2005, iTrust.

[48]  Lei Shu,et al.  ITARS: trust-aware recommender system using implicit trust networks , 2010, IET Commun..

[49]  Matthew Richardson,et al.  Trust Management for the Semantic Web , 2003, SEMWEB.

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

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

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