Warming Up to Cold Start Personalization

Smart agents face abandonment if they are unable to provide value to the users from the very first interaction. Existing smart agents take time to learn about new users before they can offer them personalized services. We present a method for learning personalization information about users quickly and without placing unnecessary hardship on them. Our method enables smart agents to pick which questions to ask the user when they first interact to maximize the agent's overall knowledge about the user. We demonstrate our method on two publically available US census datasets containing 172 user variables from 1,799,394 training and 1,618,489 testing users. The questions selected using our method improve the agent's accuracy when inferring information about future users, including information that they did not ask about. Our work enables smart agents that assist the user with personalized services soon after they start interacting.

[1]  Zoubin Ghahramani,et al.  Cold-start Active Learning with Robust Ordinal Matrix Factorization , 2014, ICML.

[2]  Francesco Ricci,et al.  Mobile Recommender Systems , 2010, J. Inf. Technol. Tour..

[3]  Andreas Krause,et al.  Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies , 2008, J. Mach. Learn. Res..

[4]  Lars Schmidt-Thieme,et al.  Learning Attribute-to-Feature Mappings for Cold-Start Recommendations , 2010, 2010 IEEE International Conference on Data Mining.

[5]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[6]  Andrew F. Monk,et al.  Theory of Personalization of Appearance: Why Users Personalize Their PCs and Mobile Phones , 2003, Hum. Comput. Interact..

[7]  G. Sridhar.  Consumer Involvement in Product Choice - A Demographic Analysis , 2007 .

[8]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[9]  L. van der Gaag,et al.  Selective evidence gathering for diagnostic belief networks , 1993 .

[10]  Mingxuan Sun,et al.  Learning multiple-question decision trees for cold-start recommendation , 2013, WSDM.

[11]  Jing-Yu Yang,et al.  N-dimensional Markov random field prior for cold-start recommendation , 2016, Neurocomputing.

[12]  Berkeley J. Dietvorst,et al.  Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err , 2014, Journal of experimental psychology. General.

[13]  Stathes Hadjiefthymiades,et al.  Facing the cold start problem in recommender systems , 2014, Expert Syst. Appl..

[14]  Mark E. Slama,et al.  Selected Socioeconomic and Demographic Characteristics Associated with Purchasing Involvement , 1985 .

[15]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[16]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .

[17]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

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

[19]  H. Spencer-Oatey.  (Im)Politeness, Face and Perceptions of Rapport: Unpackaging their Bases and Interrelationships , 2005 .

[20]  Andreas Krause,et al.  Near-optimal Nonmyopic Value of Information in Graphical Models , 2005, UAI.

[21]  Pushmeet Kohli,et al.  Tractability: Practical Approaches to Hard Problems , 2013 .

[22]  Jin-Hyuk Hong,et al.  Understanding and prediction of mobile application usage for smart phones , 2012, UbiComp.

[23]  Nathaniel Good,et al.  Naïve filterbots for robust cold-start recommendations , 2006, KDD '06.

[24]  Anh Duc Duong,et al.  Addressing cold-start problem in recommendation systems , 2008, ICUIMC '08.

[25]  Alkis Gotovos,et al.  Non-Monotone Adaptive Submodular Maximization , 2015, IJCAI.

[26]  Andreas Krause,et al.  A Utility-Theoretic Approach to Privacy in Online Services , 2010, J. Artif. Intell. Res..

[27]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..