Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Bayesian approach toward active learning for collaborative filtering
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
IEEE Transactions on Knowledge and Data Engineering
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Personalized active learning for collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Tied boltzmann machines for cold start recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Learning preferences of new users in recommender systems: an information theoretic approach
ACM SIGKDD Explorations Newsletter
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Factorizing personalized Markov chains for next-basket recommendation
Proceedings of the 19th international conference on World wide web
On bootstrapping recommender systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Adaptive bootstrapping of recommender systems using decision trees
Proceedings of the fourth ACM international conference on Web search and data mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Active collaborative filtering
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Exploiting the characteristics of matrix factorization for active learning in recommender systems
Proceedings of the sixth ACM conference on Recommender systems
PRemiSE: personalized news recommendation via implicit social experts
Proceedings of the 21st ACM international conference on Information and knowledge management
Learning multiple-question decision trees for cold-start recommendation
Proceedings of the sixth ACM international conference on Web search and data mining
Adapting to natural rating acquisition with combined active learning strategies
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Addressing cold-start in app recommendation: latent user models constructed from twitter followers
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
SoCo: a social network aided context-aware recommender system
Proceedings of the 22nd international conference on World Wide Web
Interactive collaborative filtering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Context-aware review helpfulness rating prediction
Proceedings of the 7th ACM conference on Recommender systems
Interview process learning for top-n recommendation
Proceedings of the 7th ACM conference on Recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Semi-supervised discriminative preference elicitation for cold-start recommendation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Measuring and addressing the impact of cold start on associative tag recommenders
Proceedings of the 19th Brazilian symposium on Multimedia and the web
Personalized news recommendation via implicit social experts
Information Sciences: an International Journal
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Facing the cold start problem in recommender systems
Expert Systems with Applications: An International Journal
A Monte Carlo algorithm for cold start recommendation
Proceedings of the 23rd international conference on World wide web
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A key challenge in recommender system research is how to effectively profile new users, a problem generally known as cold-start recommendation. Recently the idea of progressively querying user responses through an initial interview process has been proposed as a useful new user preference elicitation strategy. In this paper, we present functional matrix factorization (fMF), a novel cold-start recommendation method that solves the problem of initial interview construction within the context of learning user and item profiles. Specifically, fMF constructs a decision tree for the initial interview with each node being an interview question, enabling the recommender to query a user adaptively according to her prior responses. More importantly, we associate latent profiles for each node of the tree --- in effect restricting the latent profiles to be a function of possible answers to the interview questions --- which allows the profiles to be gradually refined through the interview process based on user responses. We develop an iterative optimization algorithm that alternates between decision tree construction and latent profiles extraction as well as a regularization scheme that takes into account of the tree structure. Experimental results on three benchmark recommendation data sets demonstrate that the proposed fMF algorithm significantly outperforms existing methods for cold-start recommendation.