Analyzing query reformulation data using multi-level modeling

This study explores the adoption of multi-level modeling to analyze query reformulation data. Thus far, the dependency among query reformulations within the same search session has not been adequately treated in the experimental design. This has limited the analysis of users’ query behavior. This study introduces multi-level modeling to query reformulation data analysis. Multi-level modeling is capable of handling the correlations among query reformulations and provides an avenue to analyzing the nested data structure. A demonstration of fitting query reformulation data to two types of multi-level models is provided. The method introduced in this study provides a potential solution to the analysis of query reformulations.

[1]  Soohyung Joo,et al.  Effects of topic familiarity and search skills on query reformulation behavior , 2013, ASIST.

[2]  M. Aitkin,et al.  Statistical Modelling Issues in School Effectiveness Studies , 1986 .

[3]  Soo Young Rieh,et al.  Analysis of multiple query reformulations on the web: The interactive information retrieval context , 2006, Information Processing & Management.

[4]  Jacek Gwizdka,et al.  Analysis of Query Reformulation Types on Different Search Tasks , 2010 .

[5]  D. A. Kenny,et al.  Consequences of violating the independence assumption in analysis of variance. , 1986 .

[6]  J. Ware,et al.  Random-effects models for longitudinal data. , 1982, Biometrics.

[7]  Najafi Azadeh,et al.  REAL LIFE, REAL USERS AND REAL NEEDS: A STUDY AND ANALYSIS OF USER QUERIES ON THE WEB , 2008 .

[8]  W. Bruce Croft,et al.  Combining the language model and inference network approaches to retrieval , 2004, Inf. Process. Manag..

[9]  D. Hofmann.  An Overview of the Logic and Rationale of Hierarchical Linear Models , 1997 .

[10]  S. Raudenbush,et al.  Application of Hierarchical Linear Models to Assessing Change , 1987 .

[11]  Amanda Spink,et al.  Patterns of query reformulation during Web searching , 2009, J. Assoc. Inf. Sci. Technol..

[12]  C. Eisenhart.  The assumptions underlying the analysis of variance. , 1947, Biometrics.

[13]  W. S. Robinson.  Ecological correlations and the behavior of individuals. , 1950, International journal of epidemiology.

[14]  M. Ravindranathan,et al.  STRUCTURAL EFFECTS I , 1977 .

[15]  Soohyung Joo,et al.  Assessing effectiveness of query reformulations: Analysis of user-generated information retrieval diaries , 2011, ASIST.

[16]  Amanda Spink,et al.  Real life, real users, and real needs: a study and analysis of user queries on the web , 2000, Inf. Process. Manag..

[17]  James A. Davis,et al.  A technique for analyzing the effects of group composition. , 1961 .

[18]  H. Blalock,et al.  Contextual-Effects Models: Theoretical and Methodological Issues , 1984 .

[19]  Amanda Spink,et al.  Searching the Web: the public and their queries , 2001 .

[20]  Amanda Spink,et al.  Query Modifications Patterns During Web Searching , 2007, Fourth International Conference on Information Technology (ITNG'07).

[21]  Kun Lu,et al.  Explicitly integrating MeSH thesaurus help into health information retrieval systems: An empirical user study , 2014, Inf. Process. Manag..