A Proposed Business Intelligent Framework for Recommender Systems

In this Internet age, recommender systems (RS) have become popular, offering new opportunities and challenges to the business world. With a continuous increase in global competition, e-businesses, information portals, social networks and more, websites are required to become more user-centric and rely on the presence and role of RS in assisting users in better decision making. However, with continuous changes in user interests and consumer behavior patterns that are influenced by easy access to vast information and social factors, raising the quality of recommendations has become a challenge for recommender systems. There is a pressing need for exploring hybrid models of the five main types of RS, namely collaborative, demographic, utility, content and knowledge based approaches along with advancements in Big Data (BD) to become more context-aware of the technology and social changes and to behave intelligently. There is a gap in literature with a research focus in this direction. This paper takes a step to address this by exploring a new paradigm of applying business intelligence (BI) concepts to RS for intelligently responding to user changes and business complexities. A BI based framework adopting a hybrid methodology for RS is proposed with a focus on enhancing the RS performance. Such a business intelligent recommender system (BIRS) can adopt On-line Analytical Processing (OLAP) tools and performance monitoring metrics using data mining techniques of BI to enhance its own learning, user profiling and predictive models for making a more useful set of personalised recommendations to its users. The application of the proposed framework to a B2C e-commerce case example is presented.

[1]  Jie Lu,et al.  A Fuzzy Preference Tree-Based Recommender System for Personalized Business-to-Business E-Services , 2015, IEEE Transactions on Fuzzy Systems.

[2]  William M. Campbell,et al.  Recommender Systems for the Department of Defense and Intelligence Community , 2016 .

[3]  Sitalakshmi Venkatraman,et al.  SQL Versus NoSQL Movement with Big Data Analytics , 2016 .

[4]  Wesley W. Chu,et al.  A Social Network-Based Recommender System (SNRS) , 2010, Data Mining for Social Network Data.

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

[6]  Tsan-sheng Hsu,et al.  Privacy-Preserving Collaborative Recommender Systems , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  Symeon Papavassiliou,et al.  Quality of Experience-based museum touring: a human in the loop approach , 2017, Social Network Analysis and Mining.

[8]  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.

[9]  Jean-Charles Marty,et al.  A Contact Recommender System for a Mediated Social Media , 2004, ICEIS.

[10]  Alejandro Bellogín,et al.  An empirical comparison of social, collaborative filtering, and hybrid recommenders , 2013, TIST.

[11]  Symeon Papavassiliou,et al.  Modelling museum visitors' Quality of Experience , 2016, 2016 11th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP).

[12]  Jinghua Huang,et al.  A Survey of E-Commerce Recommender Systems , 2007, 2007 International Conference on Service Systems and Service Management.

[13]  Judith Masthoff,et al.  A Survey of Explanations in Recommender Systems , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.

[14]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

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

[16]  Sitalakshmi Venkatraman,et al.  Intelligent Information Retrieval and Recommender System Framework , 2013 .

[17]  Guillermo Jiménez-Díaz,et al.  Social factors in group recommender systems , 2013, TIST.

[18]  Marie-Jean Meurs,et al.  Using Collaborative Tagging for Text Classification: From Text Classification to Opinion Mining , 2013, Informatics.

[19]  Mehrbakhsh Nilashi,et al.  Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system , 2014, Knowl. Based Syst..

[20]  Xavier Amatriain,et al.  Data Mining Methods for Recommender Systems , 2011, Recommender Systems Handbook.

[21]  Jae Kyeong Kim,et al.  A literature review and classification of recommender systems research , 2012, Expert Syst. Appl..

[22]  Charu C. Aggarwal,et al.  Recommender Systems: The Textbook , 2016 .

[23]  Hengshan Wang,et al.  Study on Recommender Systems for Business-To-Business Electronic Commerce , 2005 .

[24]  Alexander Felfernig,et al.  An Empirical Study on Consumer Behavior in the Interaction with Knowledge-based Recommender Applications , 2006, The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/EEE'06).

[25]  Gerhard Friedrich,et al.  Recommender Systems - An Introduction , 2010 .

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

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