Social Marketing Meets Targeted Customers: A Typical User Selection and Coverage Perspective

The emergence of social networks has provided opportunities for both targeted marketing and viral marketing. By concentrating the efforts on a few key customers, targeted marketing could make the promotion of the items (products) much easier and more cost-effective. On the other hand, viral marketing aims at finding a set of individuals (seeds) to maximize the word-of-mouth propagation of an item. However, these two marketing strategies can only exploit some specific characteristics of the social networks, and the problem of how to combine them together to build a better, stronger business is still open. To that end, in this paper, we propose a general approach for integrated marketing. Specifically, to market a given item, we first generate the item-specific candidate users by a recommendation algorithm, and then select the typical users who have the best balanced utility scores and consumption/social entropy. Next, treating typical users as targeted customers, we study the problem of maximizing information awareness in viral marketing with these constrained targets. Along this line, we define it as a constrained coverage maximization problem, and propose three solutions: GMIC, LMIC and QMIC. Finally, extensive experimental results on real-world datasets demonstrate that our integrated marketing approach could outperform the methods that consider only targeted marketing or viral marketing.

[1]  Mark S. Granovetter Threshold Models of Collective Behavior , 1978, American Journal of Sociology.

[2]  Maurice Queyranne,et al.  An Exact Algorithm for Maximum Entropy Sampling , 1995, Oper. Res..

[3]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[4]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[5]  Hooman Estelami,et al.  The computational effect of price endings in multi‐dimensional price advertising , 1999 .

[6]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[7]  Ronald R. Yager,et al.  Targeted E-commerce Marketing Using Fuzzy Intelligent Agents , 2000, IEEE Intell. Syst..

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

[9]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .

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

[11]  Vijay V. Vazirani,et al.  Approximation Algorithms , 2001, Springer Berlin Heidelberg.

[12]  R. Dale Wilson Using online databases for developing prioritized sales leads , 2003 .

[13]  P. Massa,et al.  Trust-aware Bootstrapping of Recommender Systems , 2006 .

[14]  Andreas Krause,et al.  Cost-effective outbreak detection in networks , 2007, KDD '07.

[15]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

[16]  Ravi Kumar,et al.  Influence and correlation in social networks , 2008, KDD.

[17]  Robert K. Plice,et al.  Toward a Sustainable Email Marketing Infrastructure , 2008 .

[18]  Vahab S. Mirrokni,et al.  Optimal marketing strategies over social networks , 2008, WWW.

[19]  Dale Schuurmans,et al.  Fast normalized cut with linear constraints , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Martin Ester,et al.  TrustWalker: a random walk model for combining trust-based and item-based recommendation , 2009, KDD.

[21]  Li Xiu,et al.  Application of data mining techniques in customer relationship management: A literature review and classification , 2009, Expert Syst. Appl..

[22]  Hadas Shachnai,et al.  Maximizing submodular set functions subject to multiple linear constraints , 2009, SODA.

[23]  Lior Rokach,et al.  Recommender Systems Handbook , 2010 .

[24]  Lars Backstrom,et al.  Find me if you can: improving geographical prediction with social and spatial proximity , 2010, WWW '10.

[25]  Wei Chen,et al.  Scalable influence maximization for prevalent viral marketing in large-scale social networks , 2010, KDD.

[26]  Damon Horowitz,et al.  The anatomy of a large-scale social search engine , 2010, WWW '10.

[27]  Laks V. S. Lakshmanan,et al.  SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model , 2011, 2011 IEEE 11th International Conference on Data Mining.

[28]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[29]  Charu C. Aggarwal,et al.  On Flow Authority Discovery in Social Networks , 2011, SDM.

[30]  Hui Lin,et al.  A Class of Submodular Functions for Document Summarization , 2011, ACL.

[31]  Kyomin Jung,et al.  IRIE: Scalable and Robust Influence Maximization in Social Networks , 2011, 2012 IEEE 12th International Conference on Data Mining.

[32]  Yuanyuan Tian,et al.  Event-based social networks: linking the online and offline social worlds , 2012, KDD.

[33]  Enhong Chen,et al.  Ensemble Pruning via Constrained Eigen-Optimization , 2012, 2012 IEEE 12th International Conference on Data Mining.

[34]  Hui Xiong,et al.  PageRank with Priors: An Influence Propagation Perspective , 2013, IJCAI.

[35]  Philip S. Yu,et al.  Empirical Evaluation of Profile Characteristics for Gender Classification on Twitter , 2013, 2013 12th International Conference on Machine Learning and Applications.

[36]  Younes Benslimane,et al.  Conducting Efficient and Cost-Effective Targeted Marketing Using Data Mining Techniques , 2013, 2013 Fourth Global Congress on Intelligent Systems.

[37]  Laks V. S. Lakshmanan,et al.  Information and Influence Propagation in Social Networks , 2013, Synthesis Lectures on Data Management.

[38]  Li Guo,et al.  UBLF: An Upper Bound Based Approach to Discover Influential Nodes in Social Networks , 2013, 2013 IEEE 13th International Conference on Data Mining.

[39]  Enhong Chen,et al.  From Social User Activities to People Affiliation , 2013, 2013 IEEE 13th International Conference on Data Mining.

[40]  Bart Thomee,et al.  Automatic selection of social media responses to news , 2013, KDD.

[41]  I-Hsien Ting,et al.  Discovering interest groups for marketing in virtual communities: An integrated approach , 2013 .

[42]  Bengt J. Nilsson,et al.  Using maximum coverage to optimize recommendation systems in e-commerce , 2013, RecSys.

[43]  Wei Wu,et al.  Diversifying Tag Selection Result for Tag Clouds by Enhancing both Coverage and Dissimilarity , 2013, WISE.

[44]  Peng Zhang,et al.  Minimizing seed set selection with probabilistic coverage guarantee in a social network , 2014, KDD.

[45]  Rashad Yazdanifard,et al.  How Social Media Marketing can Influence the Profitability of an Online Company From a Consumer Point of View , 2014 .

[46]  Enhong Chen,et al.  Mobile App Classification with Enriched Contextual Information , 2014, IEEE Transactions on Mobile Computing.

[47]  Hui Xiong,et al.  Mobile app recommendations with security and privacy awareness , 2014, KDD.

[48]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[49]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..