Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
One-Class Collaborative Filtering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Collaborative prediction and ranking with non-random missing data
Proceedings of the third ACM conference on Recommender systems
Using a trust network to improve top-N recommendation
Proceedings of the third ACM conference on Recommender systems
Training and testing of recommender systems on data missing not at random
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Matrix Completion from Noisy Entries
The Journal of Machine Learning Research
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Affiliation recommendation using auxiliary networks
Proceedings of the fourth ACM conference on Recommender systems
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Circle-based recommendation in online social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
Active learning and search on low-rank matrices
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Nearest neighbour based social recommendation using heat diffusion
Proceedings of the 6th ACM India Computing Convention
A survey of collaborative filtering based social recommender systems
Computer Communications
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Recommendation accuracy can be improved by incorporating trust relationships derived from social networks. Most recent work on social network based recommendation is focused on minimizing the root mean square error (RMSE). Social network based top-k recommendation, which recommends to a user a small number of items at a time, is not well studied. In this paper, we conduct a comprehensive study on improving the accuracy of top-k recommendation using social networks. We first show that the existing social-trust enhanced Matrix Factorization (MF) models can be tailored for top-k recommendation by including observed and missing ratings in their training objective functions. We also propose a Nearest Neighbor (NN) based top-k recommendation method that combines users' neighborhoods in the trust network with their neighborhoods in the latent feature space. Experimental results on two publicly available datasets show that social networks can significantly improve the top-k hit ratio, especially for cold start users. Surprisingly, we also found that the technical approach for combining feedback data (e.g. ratings) with social network information that works best for minimizing RMSE works poorly for maximizing the hit ratio, and vice versa.