Context based Recommendation Methods: A Brief Review

Recommendation systems consist of methods for recommending products or any items that are of interest to users in web applications for personalized experience. The recommendation helps the users to reduce the time and complexity of searching for the required information. The recommendation methods use the information of users and items as well as users’ past history of interaction to suggest preferred items. The context based methods use the situation about the user, item or interaction to give recommendations to users. Currently with the growth of techniques in acquiring the information of interaction of users with the system, the context based methods for recommendation improve the quality of recommendation. A brief review of the approaches and methods for context based recommendation is presented here with the challenges and future directions.

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

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

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

[4]  Bernd Ludwig,et al.  InCarMusic: Context-Aware Music Recommendations in a Car , 2011, EC-Web.

[5]  Francesco Ricci,et al.  Experimental evaluation of context-dependent collaborative filtering using item splitting , 2013, User Modeling and User-Adapted Interaction.

[6]  D. R. Ramesh Babu,et al.  A Novel Scheme for Term Weighting in Text Categorization: Positive Impact Factor , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[7]  María N. Moreno García,et al.  A hybrid recommendation approach for a tourism system , 2013, Expert Syst. Appl..

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

[9]  Cihan Kaleli,et al.  A multi-criteria item-based collaborative filtering framework , 2014, 2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE).

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

[11]  Alexander Tuzhilin,et al.  Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems , 2009, RecSys '09.

[12]  Gediminas Adomavicius,et al.  Context-aware recommender systems , 2008, RecSys '08.

[13]  Oscar Castillo,et al.  Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization , 2015, Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization.

[14]  Juan-Zi Li,et al.  Typicality-Based Collaborative Filtering Recommendation , 2014, IEEE Transactions on Knowledge and Data Engineering.

[15]  Jochen Nessel,et al.  The MovieOracle - Content Based Movie Recommendations , 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[16]  CHHAVI RANA,et al.  Building a Book Recommender system using time based content filtering , 2012 .

[17]  Yi Li,et al.  A hybrid recommendation algorithm adapted in e-learning environments , 2012, World Wide Web.

[18]  Yongmoo Suh,et al.  A personalized trustworthy seller recommendation in an open market , 2013, Expert Syst. Appl..

[19]  Fang Dong,et al.  A Personalized Hybrid Recommendation System Oriented to E-Commerce Mass Data in the Cloud , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[20]  Mario García Valdez,et al.  A Pre-filtering Based Context-Aware Recommender System using Fuzzy Rules , 2015, Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization.

[21]  Chin-Feng Lai,et al.  A M-Learning Content Recommendation Service by Exploiting Mobile Social Interactions , 2014, IEEE Transactions on Learning Technologies.

[22]  Amit Kumar,et al.  CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTAL , 2013 .

[23]  Gediminas Adomavicius,et al.  Incorporating contextual information in recommender systems using a multidimensional approach , 2005, TOIS.

[24]  Hao Wu,et al.  Context-Aware Recommendation via Graph-Based Contextual Modeling and Postfiltering , 2015, Int. J. Distributed Sens. Networks.

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

[26]  Michele Gorgoglione,et al.  A Contextual Modeling Approach to Context-Aware Recommender Systems , 2011 .

[27]  Pasquale Lops,et al.  Leveraging the linkedin social network data for extracting content-based user profiles , 2011, RecSys '11.

[28]  Bernd Ludwig,et al.  Matrix factorization techniques for context aware recommendation , 2011, RecSys '11.

[29]  Joseph A. Konstan,et al.  Introduction to recommender systems , 2008, SIGMOD Conference.

[30]  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.

[31]  Charu C. Aggarwal,et al.  Content-Based Recommender Systems , 2016 .