Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning multiple graphs for document recommendations
Proceedings of the 17th international conference on World Wide Web
TANGENT: a novel, 'Surprise me', recommendation algorithm
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A Probabilistic Semantic Based Mixture Collaborative Filtering
UIC '09 Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing
Detecting and ordering salient regions
Data Mining and Knowledge Discovery
An enhanced semi-supervised recommendation model based on green's function
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Community discovery using nonnegative matrix factorization
Data Mining and Knowledge Discovery
Challenging the long tail recommendation
Proceedings of the VLDB Endowment
Product recommendation with temporal dynamics
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
A novel scheme for item-based recommendation is proposed in this paper. In our framework, the items are described by an undirected weighted graph G = (V, E). V is the node set which is identical to the item set, and E is the edge set. Associate with each edge e_ij \inE is a weight w_ij \geqslant0, which represents similarity between items i and j. Without the loss of generality, we assume that any user's ratings to the items should be sufficiently smooth with respect to the intrinsic structure of the items, i.e., a user should give similar ratings to similar items. A simple algorithm is presented to achieve such a "smooth" solution. Encouraging experimental results are provided to show the effectiveness of our method.