Preference elicitation as an optimization problem

The new user coldstart problem arises when a recommender system does not yet have any information about a user. A common solution to it is to generate a profile by asking the user to rate a number of items. Which items are selected determines the quality of the recommendations made, and thus has been studied extensively. We propose a new elicitation method to generate a static preference questionnaire (SPQ) that poses relative preference questions to the user. Using a latent factor model, we show that SPQ improves personalized recommendations by choosing a minimal and diverse set of questions. We are the first to rigorously prove which optimization task should be solved to select each question in static questionnaires. Our theoretical results are confirmed by extensive experimentation. We test the performance of SPQ on two real-world datasets, under two experimental conditions: simulated, when users behave according to a latent factor model (LFM), and real, in which only real user judgments are revealed as the system asks questions. We show that SPQ reduces the necessary length of a questionnaire by up to a factor of three compared to state-of-the-art preference elicitation methods. Moreover, solving the right optimization task, SPQ also performs better than baselines with dynamically generated questions.

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

[2]  Hans Schwerdtfeger,et al.  Introduction to linear algebra and the theory of matrices , 1950 .

[3]  Jaap Kamps,et al.  The Continuous Cold-start Problem in e-Commerce Recommender Systems , 2015, CBRecSys@RecSys.

[4]  Sean M. McNee,et al.  Getting to know you: learning new user preferences in recommender systems , 2002, IUI '02.

[5]  Dean P. Foster,et al.  Clustering Methods for Collaborative Filtering , 1998, AAAI 1998.

[6]  Yong Yu,et al.  Interview process learning for top-n recommendation , 2013, RecSys.

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

[8]  Francesco Ricci,et al.  Pairwise Preferences Based Matrix Factorization and Nearest Neighbor Recommendation Techniques , 2016, RecSys.

[9]  Ron Kohavi,et al.  Lazy Decision Trees , 1996, AAAI/IAAI, Vol. 1.

[10]  S. Goreinov,et al.  How to find a good submatrix , 2010 .

[11]  Nuria Oliver,et al.  I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems , 2009, UMAP.

[12]  F. Maxwell Harper,et al.  The MovieLens Datasets: History and Context , 2016, TIIS.

[13]  Oren Somekh,et al.  Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design , 2014, WWW.

[14]  John Riedl,et al.  Learning preferences of new users in recommender systems: an information theoretic approach , 2008, SKDD.

[15]  Armelle Brun,et al.  Comparisons Instead of Ratings: Towards More Stable Preferences , 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[16]  Ömer Egecioglu,et al.  Approximating the Diameter of a Set of Points in the Euclidean Space , 1989, Inf. Process. Lett..

[17]  Anton van den Hengel,et al.  Image-Based Recommendations on Styles and Substitutes , 2015, SIGIR.

[18]  Wei Chu,et al.  A contextual-bandit approach to personalized news article recommendation , 2010, WWW '10.

[19]  Amit Sharma,et al.  Pairwise learning in recommendation: experiments with community recommendation on linkedin , 2013, RecSys.

[20]  Djoerd Hiemstra,et al.  Beyond Movie Recommendations: Solving the Continuous Cold Start Problem in E-commerceRecommendations , 2016, ArXiv.

[21]  Marco Pellegrini,et al.  On computing the diameter of a point set in high dimensional Euclidean space , 1999, Theor. Comput. Sci..

[22]  Mehrbakhsh Nilashi,et al.  Collaborative filtering recommender systems , 2013 .

[23]  William W. Hager,et al.  Updating the Inverse of a Matrix , 1989, SIAM Rev..

[24]  Gleb Gusev,et al.  Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

[25]  Yehuda Koren,et al.  On bootstrapping recommender systems , 2010, CIKM.

[26]  Filip Radlinski,et al.  Simple Personalized Search Based on Long-Term Behavioral Signals , 2017, ECIR.

[27]  Chao Liu,et al.  Wisdom of the better few: cold start recommendation via representative based rating elicitation , 2011, RecSys '11.

[28]  Filip Radlinski,et al.  Towards Conversational Recommender Systems , 2016, KDD.

[29]  Slava Kisilevich,et al.  Initial Profile Generation in Recommender Systems Using Pairwise Comparison , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[30]  Yehuda Koren,et al.  Adaptive bootstrapping of recommender systems using decision trees , 2011, WSDM '11.