Numerical Facet Range Partition: Evaluation Metric and Methods

Faceted navigation is a very useful component in today's search engines. It is especially useful when user has an exploratory information need or prefer certain attribute values than others. Existing work has tried to optimize faceted systems in many aspects, but little work has been done on optimizing numerical facet ranges (e.g., price ranges of product). In this paper, we introduce for the first time the research problem on numerical facet range partition and formally frame it as an optimization problem. To enable quantitative evaluation of a partition algorithm, we propose an evaluation metric to be applied to search engine logs. We further propose two range partition algorithms that computationally optimize the defined metric. Experimental results on a two-month search log from a major e-Commerce engine show that our proposed method can significantly outperform baseline.

[1]  Peter Pirolli,et al.  Information Foraging , 2009, Encyclopedia of Database Systems.

[2]  Matthew Banta,et al.  What do exploratory searchers look at in a faceted search interface? , 2009, JCDL '09.

[3]  Jaana Kekäläinen,et al.  Cumulated gain-based evaluation of IR techniques , 2002, TOIS.

[4]  John D. Lafferty,et al.  A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval , 2017, SIGF.

[5]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[6]  Ronny Lempel,et al.  Approximately optimal facet selection , 2012, SAC '12.

[7]  Vagelis Hristidis,et al.  FACeTOR: cost-driven exploration of faceted query results , 2010, CIKM.

[8]  Rong Jin,et al.  Learning to Rank by Optimizing NDCG Measure , 2009, NIPS.

[9]  J Allan,et al.  Readings in information retrieval. , 1998 .

[10]  J. Kiefer,et al.  Asymptotic Minimax Character of the Sample Distribution Function and of the Classical Multinomial Estimator , 1956 .

[11]  Yinan Zhang,et al.  Information Retrieval as Card Playing: A Formal Model for Optimizing Interactive Retrieval Interface , 2015, SIGIR.

[12]  S. Robertson.  The probability ranking principle in IR , 1997 .

[13]  Yi Chang,et al.  Learning to rank related entities in Web search , 2015, Neurocomputing.

[14]  Chinmay Hegde,et al.  Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms , 2015, PODS.

[15]  D. Anderson,et al.  Algorithms for minimization without derivatives , 1974 .

[16]  David J. DeWitt,et al.  Equi-depth multidimensional histograms , 1988, SIGMOD '88.

[17]  Marti A. Hearst.  Search User Interfaces , 2009 .

[18]  Yi Zhang,et al.  Personalized interactive faceted search , 2008, WWW.

[19]  Filip Radlinski,et al.  Relevance and Effort: An Analysis of Document Utility , 2014, CIKM.

[20]  Marti A. Hearst.  UIs for Faceted Navigation Recent Advances and Remaining Open Problems , 2008 .

[21]  Nélida E. Echebest,et al.  Active-set strategy in Powell's method for optimization without derivatives , 2011 .

[22]  References , 1971 .

[23]  Leif Azzopardi,et al.  Modelling interaction with economic models of search , 2014, SIGIR.

[24]  Uzay Kaymak,et al.  Facet selection algorithms for web product search , 2013, CIKM.

[25]  Alistair Moffat,et al.  Rank-biased precision for measurement of retrieval effectiveness , 2008, TOIS.

[26]  Lixing Han,et al.  Implementing the Nelder-Mead simplex algorithm with adaptive parameters , 2010, Computational Optimization and Applications.

[27]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[28]  Torsten Suel,et al.  Optimal Histograms with Quality Guarantees , 1998, VLDB.

[29]  Roelof van Zwol,et al.  Faceted exploration of image search results , 2010, WWW '10.

[30]  Nick Craswell,et al.  An experimental comparison of click position-bias models , 2008, WSDM '08.

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

[32]  Filip Radlinski,et al.  A support vector method for optimizing average precision , 2007, SIGIR.

[33]  Mukesh K. Mohania,et al.  Retrieval]: Query formulation, search process , 2022 .