SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
One-Class Collaborative Filtering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
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
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Circle-based recommendation in online social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A survey of collaborative filtering based social recommender systems
Computer Communications
| Hi-index | 0.00 |
Collaborative filtering (CF) is an effective recommendation technique, which selects items for an individual user based on similar users' preferences. However, CF may not fully reflect the procedure how people choose an item in real life, for users are more likely to ask friends for opinions instead of asking similar strangers. Recently, some recommendation methods based on social network have been raised. These approaches incorporate social network into the CF algorithms and users' preferences can be influenced by the favors of their friends. These social approaches require the knowledge of similarities among friends. There are two popular similarity functions: Vector Space Similarity (VSS) and Pearson Correlation Coefficient (PCC). However, both friends similarity functions are based on the item-sets they rated in common. In most cases, these functions are impractical, i.e. if two friends do not share the same items in common, the similarity between them will be zeros. To solve this problem, we propose an Adaptive Social Similarity (ASS) function based on the matrix factorization technique. We conduct our experiment on a large dataset: Epinions, which is a widely-used dataset with social information. The experiment results illustrate that our approach outperforms the baseline models and achieves a better performance than social-based method in [4].