Discovering cohesive subgroups from social networks for targeted advertising

In this paper, we propose a framework that utilizes the concept of a social network for the targeted advertising of products. This approach discovers the cohesive subgroups from a customer's social network as derived from the customer's interaction data, and uses them to infer the probability of a customer preferring a product category from transaction records. This information is then used to construct a targeted advertising system. We evaluate the proposed approach by using both synthetic data and real-world data. The experimental results show that our approach does well at recommending relevant products.

[1]  Barry Wellman,et al.  For a social network analysis of computer networks: a sociological perspective on collaborative work and virtual community , 1996, SIGCPR '96.

[2]  Jenny Preece,et al.  Online Communities: Designing Usability and Supporting Sociability , 2000 .

[3]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[4]  Joseph A. Konstan,et al.  Content-Independent Task-Focused Recommendation , 2001, IEEE Internet Comput..

[5]  R. Kohli,et al.  Internet Recommendation Systems , 2000 .

[6]  Shoshana Loeb,et al.  Information filtering , 1992, CACM.

[7]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[8]  Atsuyoshi Nakamura,et al.  Improvements in practical aspects of optimally scheduling web advertising , 2002, WWW '02.

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

[10]  John Guare,et al.  Six Degrees of Separation: A Play , 1990 .

[11]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[12]  Sergio A. Alvarez,et al.  Efficient Adaptive-Support Association Rule Mining for Recommender Systems , 2004, Data Mining and Knowledge Discovery.

[13]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[14]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[15]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[16]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[17]  Robert F. Easley,et al.  A SURVEY OF RECOMMENDATION SYSTEMS IN ELECTRONIC COMMERCE , 2001 .

[18]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[19]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[20]  P. Kotler,et al.  Marketing: An Introduction , 1997 .

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

[22]  Hsinchun Chen,et al.  A graph model for E-commerce recommender systems , 2004, J. Assoc. Inf. Sci. Technol..

[23]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[24]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[25]  Caroline Haythornthwaite,et al.  Studying Online Social Networks , 2006, J. Comput. Mediat. Commun..

[26]  Chuck Lam,et al.  SNACK: incorporating social network information in automated collaborative filtering , 2004, EC '04.

[27]  Bart Selman,et al.  Referral Web: combining social networks and collaborative filtering , 1997, CACM.

[28]  S H Strogatz,et al.  Random graph models of social networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[29]  John A. Tomlin,et al.  An entropy approach to unintrusive targeted advertising on the Web , 2000, Comput. Networks.

[30]  Yossi Matias,et al.  Scheduling space-sharing for internet advertising , 2002, Journal of Scheduling.

[31]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[32]  Naoki Abe,et al.  Unintrusive Customization Techniques for Web Advertising , 1999, Comput. Networks.

[33]  Tao Luo,et al.  Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization , 2004, Data Mining and Knowledge Discovery.