Hybrid Group Recommendation Using Modified Termite Colony Algorithm: A Context Towards Big Data

Since the introduction of Web 2.0, group recommendation systems become an effective tool for consulting and recommending items according to the choices of group of likeminded users. However, the population of dataset consisting of the large number of choices increases the size of storage. As a result, identification of the combination for specific recommendation becomes complex. Hence, the existing group recommendation system should support methodology for handling large data volume with varsity. In this paper, we propose a content-boosted modified termite colony optimisation-based rating prediction algorithm (CMTRP) for group recommendation system. CMTRP employs a hybrid recommendation framework with respect to the big data paradigm to deal with the trend of large data. The framework utilises the communal ratings that help to overcome the scalability problem. The experimental results reveal that CMTRP provides less error in the rating prediction and higher recommendation precision compared with the existing algorithms.

[1]  Panagiotis Symeonidis,et al.  MusicBox: Personalized Music Recommendation Based on Cubic Analysis of Social Tags , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[2]  Jugal K. Kalita,et al.  Towards an Unsupervised Method for Network Anomaly Detection in Large Datasets , 2014, Comput. Informatics.

[3]  Josep Lluís de la Rosa i Esteva,et al.  A Taxonomy of Recommender Agents on the Internet , 2003, Artificial Intelligence Review.

[4]  Donghee Yoo,et al.  A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis , 2012, Electron. Commer. Res. Appl..

[5]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[6]  Yinong Chen,et al.  Distributed collaborative filtering with singular ratings for large scale recommendation , 2014, J. Syst. Softw..

[7]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[8]  Liliana Ardissono,et al.  Intrigue: Personalized recommendation of tourist attractions for desktop and hand held devices , 2003, Appl. Artif. Intell..

[9]  Laura Sebastia,et al.  On the design of individual and group recommender systems for tourism , 2011, Expert Syst. Appl..

[10]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[11]  Chun Chen,et al.  Using rich social media information for music recommendation via hypergraph model , 2011, TOMCCAP.

[12]  Li Chen,et al.  Critiquing-based recommenders: survey and emerging trends , 2012, User Modeling and User-Adapted Interaction.

[13]  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..

[14]  Barry Smyth,et al.  Recommendation to Groups , 2007, The Adaptive Web.

[15]  Yoon Ho Cho,et al.  Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations , 2010, Inf. Sci..

[16]  Oscar Sanjuán Martínez,et al.  Recommendation System based on user interaction data applied to intelligent electronic books , 2011, Comput. Hum. Behav..

[17]  J. Scott Armstrong,et al.  Principles of forecasting , 2001 .

[18]  Approximation Theory and Methods (M. J. D. Powell) , 1982 .

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

[20]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

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

[22]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[23]  Dina Goren-Bar,et al.  FIT-recommend ing TV programs to family members , 2004, Comput. Graph..

[24]  Yu Li,et al.  A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce , 2005, Expert Syst. Appl..

[25]  Guy Shani,et al.  A Survey of Accuracy Evaluation Metrics of Recommendation Tasks , 2009, J. Mach. Learn. Res..

[26]  Maria Soledad Pera,et al.  A group recommender for movies based on content similarity and popularity , 2013, Inf. Process. Manag..

[27]  Félix Hernández-del-Olmo,et al.  Evaluation of recommender systems: A new approach , 2008, Expert Syst. Appl..

[28]  George Lekakos,et al.  A hybrid approach for movie recommendation , 2006, Multimedia Tools and Applications.

[29]  Juan Manuel Cueva Lovelle,et al.  Implicit feedback techniques on recommender systems applied to electronic books , 2012, Comput. Hum. Behav..

[30]  Toon De Pessemier,et al.  Hybrid group recommendations for a travel service , 2016, Multimedia Tools and Applications.

[31]  Jose Jesus Castro-Schez,et al.  A highly adaptive recommender system based on fuzzy logic for B2C e-commerce portals , 2011, Expert Syst. Appl..

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

[33]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[34]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

[35]  Keun Ho Ryu,et al.  Proposal reviewer recommendation system based on big data for a national research management institute , 2017, J. Inf. Sci..

[36]  Thorsten Hennig-Thurau,et al.  Can Automated Group Recommender Systems Help Consumers Make Better Choices? , 2012 .

[37]  Logesh Ravi,et al.  A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users , 2016, Comput. Intell. Neurosci..

[38]  Gianni Fenu,et al.  Influence of Rating Prediction on Group Recommendation's Accuracy , 2016, IEEE Intelligent Systems.

[39]  Izak Benbasat,et al.  E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact , 2007, MIS Q..

[40]  Erik Duval,et al.  Context-Aware Recommender Systems for Learning: A Survey and Future Challenges , 2012, IEEE Transactions on Learning Technologies.

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

[42]  Hsinchun Chen,et al.  A comparison of collaborative-filtering algorithms for ecommerce , 2007 .

[43]  Francesco Ricci,et al.  Contextual music information retrieval and recommendation: State of the art and challenges , 2012, Comput. Sci. Rev..

[44]  Mária Bieliková,et al.  Personalized hybrid recommendation for group of users: Top-N multimedia recommender , 2016, Inf. Process. Manag..

[45]  Jinjun Chen,et al.  KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications , 2014, IEEE Transactions on Parallel and Distributed Systems.

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

[47]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

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

[49]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[50]  Xingshe Zhou,et al.  TV Program Recommendation for Multiple Viewers Based on user Profile Merging , 2006, User Modeling and User-Adapted Interaction.

[51]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[52]  Sumit Sarkar,et al.  The Role of the Management Sciences in Research on Personalization , 2003, Manag. Sci..