Half Transductive Ranking

We study the standard retrieval task of ranking a fixed set of items given a previously unseen query and pose it as the half transductive ranking problem. The task is transductive as the set of items is fixed. Transductive representations (where the vector representation of each example is learned) allow the generation of highly nonlinear embeddings that capture object relationships without relying on a specific choice of features, and require only relatively simple optimization. Unfortunately, they have no direct outof-sample extension. Inductive approaches on the other hand allow for the representation of unknown queries. We describe algorithms for this setting which have the advantages of both transductive and inductive approaches, and can be applied in unsupervised (either reconstruction-based or graph-based) and supervised ranking setups. We show empirically that our methods give strong performance on all three tasks.

[1]  Michael Collins,et al.  New Ranking Algorithms for Parsing and Tagging: Kernels over Discrete Structures, and the Voted Perceptron , 2002, ACL.

[2]  David M. Blei,et al.  Supervised Topic Models , 2007, NIPS.

[3]  Richard A. Harshman,et al.  Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..

[4]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[5]  Geoffrey E. Hinton,et al.  Semantic hashing , 2009, Int. J. Approx. Reason..

[6]  Gregory N. Hullender,et al.  Learning to rank using gradient descent , 2005, ICML.

[7]  Kilian Q. Weinberger,et al.  Fast solvers and efficient implementations for distance metric learning , 2008, ICML '08.

[8]  Mukund Balasubramanian,et al.  The Isomap Algorithm and Topological Stability , 2002, Science.

[9]  John Guiver,et al.  Learning to rank with SoftRank and Gaussian processes , 2008, SIGIR '08.

[10]  Tao Qin,et al.  LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval , 2007 .

[11]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[12]  Tie-Yan Liu,et al.  Learning to rank: from pairwise approach to listwise approach , 2007, ICML '07.

[13]  Lawrence K. Saul,et al.  Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..

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

[15]  Kevin Duh,et al.  Learning to rank with partially-labeled data , 2008, SIGIR '08.

[16]  John Langford,et al.  Hash Kernels , 2009, AISTATS.

[17]  Michel Verleysen,et al.  Nonlinear Dimensionality Reduction , 2021, Computer Vision.

[19]  Nicolas Le Roux,et al.  Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering , 2003, NIPS.

[20]  Léon Bottou,et al.  Stochastic Learning , 2003, Advanced Lectures on Machine Learning.

[21]  Bernhard Schölkopf,et al.  Kernel Principal Component Analysis , 1997, ICANN.

[22]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[23]  Ralf Herbrich,et al.  Large margin rank boundaries for ordinal regression , 2000 .

[24]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[25]  Carey E. Priebe,et al.  The out-of-sample problem for classical multidimensional scaling , 2008, Comput. Stat. Data Anal..

[26]  Yanjun Qi,et al.  Supervised semantic indexing , 2009, ECIR.

[27]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[28]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[29]  Oren Kurland,et al.  Query-drift prevention for robust query expansion , 2008, SIGIR '08.