GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Monte Carlo Methods in PageRank Computation: When One Iteration is Sufficient
SIAM Journal on Numerical Analysis
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
A model of a trust-based recommendation system on a social network
Autonomous Agents and Multi-Agent Systems
Trust-based recommendation systems: an axiomatic approach
Proceedings of the 17th international conference on World Wide Web
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Exploiting Item Taxonomy for Solving Cold-Start Problem in Recommendation Making
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
A Log-Linear Model with Latent Features for Dyadic Prediction
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Collaborative error-reflected models for cold-start recommender systems
Decision Support Systems
Functional matrix factorizations for cold-start recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A collaborative filtering approach to mitigate the new user cold start problem
Knowledge-Based Systems
Personalized recommendation via cross-domain triadic factorization
Proceedings of the 22nd international conference on World Wide Web
Localized matrix factorization for recommendation based on matrix block diagonal forms
Proceedings of the 22nd international conference on World Wide Web
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Recommendation systems have been widely used in E-commerce sites, social networks, etc. One of the core tasks in recommendation systems is to predict the users' ratings on items. Although many models and algorithms have been proposed, how to make accurate prediction for new users with extremely few rating records still remains a big challenge, which is called the cold start problem. Many existing methods utilize additional information, such as social graphs, to cope with the cold start problem. However, the side information may not always be available. In contrast to such methods, we propose a more general solution to address the cold start problem based on the observed user rating records only. Specifically we define a random walk on a bipartite graph of users and items to simulate the preference propagation among users, in order to alleviate the data sparsity problem for cold start users. Then we propose a Monte Carlo algorithm to estimate the similarity between different users. This algorithm takes a precomputation approach, and thus can efficiently compute the user similarity given any new user for rating prediction. In addition, our algorithm can easily handle dynamic updates and can be parallelized naturally, which are crucial for large recommendation systems. Theoretical analysis is presented to demonstrate the efficiency and effectiveness of our algorithm, and extensive experiments also confirm our theoretical findings.