Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Supporting Context-Aware Media Recommendations for Smart Phones
IEEE Pervasive Computing
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
OSS: a semantic similarity function based on hierarchical ontologies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multiple feature fusion for social media applications
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Music recommendation by unified hypergraph: combining social media information and music content
Proceedings of the international conference on Multimedia
Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr
Journal of Information Science
Collection-based sparse label propagation and its application on social group suggestion from photos
ACM Transactions on Intelligent Systems and Technology (TIST)
A Graphical Model for Context-Aware Visual Content Recommendation
IEEE Transactions on Multimedia
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Data recommendation as a kind of active mode is more meaningful and important than traditional passive search mode in social media environment. The importance of contextual information has also been recognized by researchers and practitioners in many disciplines, including recommendation system, e-commerce, information retrieval, mobile computing and so on. In this paper, we propose a novel approach for context-aware social media recommendation via mining different granularities of potential groups, called Common Preference Group (CPG). Intuitively, CPG mining is to cluster those users who are interested in any topic set with certain context and have similar affection degree for each topic in the set. It means each user could belong to multiple CPG corresponding to different topic sets. The approach absorbs the characteristic of Collaborative Filtering (CF) technique but overcomes its defects, such as cold-start, data sparseness. Moreover, we build the Tag-Feature Semantic-pairs (TFS) to represent the semantic topics implied in media object to improve the accuracy of CPG mining. To evaluate the efficiency and the accuracy of our approach, we use two datasets: De is a simulated dataset and Dp is a real-life corpus collected from Flickr. The experimental results show the superiority of our approach for social media recommendation.