A Reusable Methodology for the Instantiation of Social Recommender Systems

Social recommender systems exploit the social knowledge available in social networks to provide accurate recommendations. However, their instantiation is not straightforward due to its complexity. To alleviate this development complexity, we propose a methodology based on templates that conceptualize the behavior of such applications and can be reused to create several social recommender applications in social networks. This development methodology comprises not only templates but also a generic architecture named ARISE and a collection of software components that provide the required functionality. We prove that our social templates speed up and facilitate the development process, and demonstrate the viability of our generic architecture in two different case studies.

[1]  Alfred Kobsa,et al.  Workshop on recommender systems and the social web , 2009, RecSys '09.

[2]  Balaji Rajagopalan,et al.  Knowledge-sharing and influence in online social networks via viral marketing , 2003, CACM.

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

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

[5]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[6]  Nicholas A. Christakis,et al.  Social contagion theory: examining dynamic social networks and human behavior , 2011, Statistics in medicine.

[7]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

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

[9]  Juan A. Recio-García,et al.  HappyMovie: A Facebook Application for Recommending Movies to Groups , 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

[10]  Yehuda Koren,et al.  Advances in Collaborative Filtering , 2011, Recommender Systems Handbook.

[11]  Patrick Brézillon,et al.  Understanding Context Before Using It , 2005, CONTEXT.

[12]  Juan A. Recio-Garc,et al.  Personality and Social Trust in Group Recommendations , 2010 .

[13]  Derry O'Sullivan,et al.  Improving Case-Based Recommendation: A Collaborative Filtering Approach , 2002, ECCBR.

[14]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[15]  Martin Ester,et al.  Using a trust network to improve top-N recommendation , 2009, RecSys '09.

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

[17]  Andreas Hotho,et al.  4th ACM RecSys workshop on recommender systems and the social web , 2012, RecSys '12.

[18]  Francesco Ricci,et al.  ITR: A Case-Based Travel Advisory System , 2002, ECCBR.

[19]  John Riedl,et al.  Collaborative Filtering Recommender Systems , 2011, Found. Trends Hum. Comput. Interact..

[20]  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.

[21]  Pedro Antonio González Calero,et al.  Semantic templates for case-based reasoning systems , 2009, The Knowledge Engineering Review.

[22]  Pedro A. González-Calero,et al.  Semantic templates for case-based reasoning systems , 2009, Knowl. Eng. Rev..

[23]  Pedro A. González-Calero,et al.  Template-Based Design in COLIBRI Studio , 2014, Inf. Syst..

[24]  Juan A. Recio-García,et al.  An architecture and functional description to integrate social behaviour knowledge into group recommender systems , 2014, Applied Intelligence.

[25]  Ralph H. Kilmann,et al.  Thomas-Kilmann Conflict Mode Instrument , 1974 .

[26]  Barry Smyth,et al.  iCARE: intelligent customer assistance for recommending eyewear , 2006, IUI '06.

[27]  Judith Masthoff,et al.  Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers , 2004, User Modeling and User-Adapted Interaction.

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

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

[30]  Hideo.  ExpertClerk : A Conversational Case-Based Reasoning Tool for Developing Salesclerk Agents in E-Commerce , .