Collaborative Ranking With 17 Parameters

The primary application of collaborate filtering (CF) is to recommend a small set of items to a user, which entails ranking. Most approaches, however, formulate the CF problem as rating prediction, overlooking the ranking perspective. In this work we present a method for collaborative ranking that leverages the strengths of the two main CF approaches, neighborhood- and model-based. Our novel method is highly efficient, with only seventeen parameters to optimize and a single hyperparameter to tune, and beats the state-of-the-art collaborative ranking methods. We also show that parameters learned on datasets from one item domain yield excellent results on a dataset from very different item domain, without any retraining.

[1]  Suhrid Balakrishnan,et al.  Collaborative ranking , 2012, WSDM '12.

[2]  Hang Li.  Learning to Rank for Information Retrieval and Natural Language Processing , 2011, Synthesis Lectures on Human Language Technologies.

[3]  Christopher J. C. Burges,et al.  From RankNet to LambdaRank to LambdaMART: An Overview , 2010 .

[4]  Kenneth Y. Goldberg,et al.  Eigentaste: A Constant Time Collaborative Filtering Algorithm , 2001, Information Retrieval.

[5]  Qiang Yang,et al.  Scalable collaborative filtering using cluster-based smoothing , 2005, SIGIR '05.

[6]  Quoc V. Le,et al.  Learning to Rank with Nonsmooth Cost Functions , 2006, Neural Information Processing Systems.

[7]  Jaana Kekäläinen,et al.  IR evaluation methods for retrieving highly relevant documents , 2000, SIGIR '00.

[8]  Benjamin M. Marlin,et al.  Collaborative Filtering: A Machine Learning Perspective , 2004 .

[9]  James Bennett,et al.  The Netflix Prize , 2007 .

[10]  Feng Niu,et al.  Million Song Dataset Challenge ! , 2012 .

[11]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[12]  Alexander J. Smola,et al.  Maximum Margin Matrix Factorization for Collaborative Ranking , 2007 .

[13]  Thomas Hofmann,et al.  Latent semantic models for collaborative filtering , 2004, TOIS.

[14]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[15]  Qiang Yang,et al.  EigenRank: a ranking-oriented approach to collaborative filtering , 2008, SIGIR '08.

[16]  Benjamin M. Marlin,et al.  Modeling User Rating Profiles For Collaborative Filtering , 2003, NIPS.

[17]  Eric Horvitz,et al.  Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach , 2000, UAI.

[18]  Tie-Yan Liu,et al.  Learning to rank for information retrieval , 2009, SIGIR.

[19]  Yuan Yao,et al.  Statistical ranking and combinatorial Hodge theory , 2008, Math. Program..

[20]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[21]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[22]  David F. Gleich,et al.  Rank aggregation via nuclear norm minimization , 2011, KDD.

[23]  John Riedl,et al.  An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms , 2002, Information Retrieval.

[24]  Yi Chang,et al.  Yahoo! Learning to Rank Challenge Overview , 2010, Yahoo! Learning to Rank Challenge.

[25]  Hsuan-Tien Lin,et al.  An Ensemble Ranking Solution for the Yahoo ! Learning to Rank Challenge , 2010 .