Recommendations based on network analysis

Most recommendations are made based on the computation of user specified constraints or functions of object similarity. In this paper, we discuss a new trend of recommender systems that are based on the information network analysis to exploit the relationships between data objects. An information network can be constructed from different application networks such as social media, traffic management systems, and sensor networks. Heterogeneous information networks are now ubiquitous. How to make recommendations for the evidence-based decisions based on the fusion of these information networks presents a challenge. We present a framework of recommendations based on information network analysis. The practical examples are used to demonstrate the potential of this type of recommendation techniques. The evaluation methodologies for network-base recommendations are also addressed.

[1]  G. Guyatt,et al.  Grading quality of evidence and strength of recommendations , 2004, BMJ : British Medical Journal.

[2]  Desmond J. Higham,et al.  Network Science - Complexity in Nature and Technology , 2010, Network Science.

[3]  R. Guha,et al.  Open Rating Systems , 2002 .

[4]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[5]  E. David,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World , 2010 .

[6]  Panayiotis Bozanis,et al.  Effective rank aggregation for metasearching , 2011, J. Syst. Softw..

[7]  Chen Xiaoyun,et al.  PGMCLU: A novel parallel grid-based clustering algorithm for multi-density datasets , 2009, 2009 1st IEEE Symposium on Web Society.

[8]  Wei-Ying Ma,et al.  Object-level ranking: bringing order to Web objects , 2005, WWW '05.

[9]  Moni Naor,et al.  Optimal aggregation algorithms for middleware , 2001, PODS.

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

[11]  Allan Borodin,et al.  Link analysis ranking: algorithms, theory, and experiments , 2005, TOIT.

[12]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[13]  Ramesh Sharda,et al.  Decision Support Systems , 2009, Handbook of Automation.

[14]  M. Kendall.  Rank Correlation Methods , 1949 .

[15]  Donald Kossmann,et al.  The Skyline operator , 2001, Proceedings 17th International Conference on Data Engineering.

[16]  Alistair Moffat,et al.  A similarity measure for indefinite rankings , 2010, TOIS.

[17]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[18]  Ronald Fagin,et al.  Comparing top k lists , 2003, SODA '03.

[19]  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.

[20]  Yizhou Sun,et al.  Ranking-based clustering of heterogeneous information networks with star network schema , 2009, KDD.

[21]  Ted G. Lewis,et al.  Network Science: Theory and Applications , 2009 .

[22]  Yizhou Sun,et al.  RankClus: integrating clustering with ranking for heterogeneous information network analysis , 2009, EDBT '09.

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