-Spear: A New Method for Expert Based Recommendation Systems

Recommendation systems are based on a fast and effective personalized mechanism to provide items relevant to users. In this article, an expert-based approach for recommendation is proposed. We extend the spamming-resistant expertise analysis and ranking (SPEAR) algorithm to determine a set of experts from a set of attributes and values, calling the modification the -SPEAR algorithm. This system can recommend a set of items to users using expert opinions. In this approach, we use ontology to build profiles of users. The experimental results are implemented in the movie domain as a case study. Our data set was collected from IMDB and MovieLens data sets.

[1]  Zhongfu Wu,et al.  Userrank for item-based collaborative filtering recommendation , 2011, Inf. Process. Lett..

[2]  Tim Berners-Lee,et al.  Linked Data - The Story So Far , 2009, Int. J. Semantic Web Inf. Syst..

[3]  Jair Minoro Abe,et al.  The paraconsistent process order control method , 2014, Vietnam Journal of Computer Science.

[4]  Stuart E. Middleton,et al.  Ontological user profiling in recommender systems , 2004, TOIS.

[5]  Antonina Dattolo,et al.  Automatic keyphrase extraction and ontology mining for content‐based tag recommendation , 2010, Int. J. Intell. Syst..

[6]  Bracha Shapira,et al.  AN ONTOLOGY-CONTENT-BASED FILTERING METHOD , 2008 .

[7]  Tengku Mohd Tengku Sembok,et al.  A Weighted-Profiling Using an Ontology Basefor Semantic-Based Search , 2008 .

[8]  Carl D. Meyer,et al.  Deeper Inside PageRank , 2004, Internet Math..

[9]  Jason J. Jung.  Attribute selection-based recommendation framework for short-head user group: An empirical study by MovieLens and IMDB , 2012, Expert Syst. Appl..

[10]  Ngoc Thanh Nguyen,et al.  Integrating Multiple Experts for Correction Process in InteractiveRecommendation Systems , 2012, J. Univers. Comput. Sci..

[11]  Ngoc Thanh Nguyen,et al.  A new graph-based flooding matching method for ontology integration , 2013, 2013 IEEE International Conference on Cybernetics (CYBCO).

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

[13]  Michael F. Schwartz,et al.  Discovering shared interests using graph analysis , 1993, CACM.

[14]  Ngoc Thanh Nguyen,et al.  PROCESSING INCONSISTENCY OF KNOWLEDGE IN DETERMINING KNOWLEDGE OF A COLLECTIVE , 2009, Cybern. Syst..

[15]  Gabriella Pasi,et al.  Personal ontologies: Generation of user profiles based on the YAGO ontology , 2013, Inf. Process. Manag..

[16]  Gene H. Golub,et al.  Extrapolation methods for accelerating PageRank computations , 2003, WWW '03.

[17]  Marco Gori,et al.  ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines , 2007, IJCAI.

[18]  Christoph Meinel,et al.  SPEAR: SPAMMING‐RESISTANT EXPERTISE ANALYSIS AND RANKING IN COLLABORATIVE TAGGING SYSTEMS , 2011, Comput. Intell..

[19]  Deborah L. McGuinness,et al.  owl:sameAs and Linked Data: An Empirical Study , 2010 .

[20]  Bamshad Mobasher,et al.  Learning Ontology-Based User Profiles: A Semantic Approach to Personalized Web Search , 2007, IEEE Intell. Informatics Bull..

[21]  Young-Koo Lee,et al.  OWL-Based User Preference and Behavior Routine Ontology for Ubiquitous System , 2005, OTM Conferences.

[22]  Hui Zhang,et al.  Construction of Ontology-Based User Model for Web Personalization , 2007, User Modeling.

[23]  Jason J. Jung.  Exploiting multi-agent platform for indirect alignment between multilingual ontologies: A case study on tourism business , 2011, Expert Syst. Appl..

[24]  Harith Alani,et al.  Exploiting Synergy Between Ontologies and Recommender Systems , 2002, Semantic Web Workshop.

[25]  Alejandro Bellogín,et al.  A multilayer ontology-based hybrid recommendation model , 2008, AI Commun..

[26]  Ngoc Thanh Nguyen,et al.  Integrating Multiple Experts for Correction Process in InteractiveRecommendation Systems , 2013, J. Univers. Comput. Sci..

[27]  Jason J. Jung.  Contextual synchronization for efficient social collaborations in enterprise computing: A case study on TweetPulse , 2013, Concurr. Eng. Res. Appl..

[28]  Conor Hayes,et al.  Using Linked Data to Build Open, Collaborative Recommender Systems , 2010, AAAI Spring Symposium: Linked Data Meets Artificial Intelligence.

[29]  Naren Ramakrishnan,et al.  Studying Recommendation Algorithms by Graph Analysis , 2003, Journal of Intelligent Information Systems.

[30]  Christoph Meinel,et al.  Telling experts from spammers: expertise ranking in folksonomies , 2009, SIGIR.

[31]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

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