GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Matrix computations (3rd ed.)
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
A new approach for combining content-based and collaborative filters
Journal of Intelligent Information Systems
IEEE Transactions on Knowledge and Data Engineering
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Major components of the gravity recommendation system
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Simrank++: query rewriting through link analysis of the click graph
Proceedings of the VLDB Endowment
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Random walk based entity ranking on graph for multidimensional recommendation
Proceedings of the fifth ACM conference on Recommender systems
The efficient imputation method for neighborhood-based collaborative filtering
Proceedings of the 21st ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
Several key applications like recommender systems deal with data in the form of ratings made by users on items. In such applications, one of the most crucial tasks is to find users that share common interests, or items with similar characteristics. Assessing the similarity between users or items has several valuable uses, among which are the recommendation of new items, the discovery of groups of like-minded individuals, and the automated categorization of items. It has been recognized that popular methods to compute similarities, based on correlation, are not suitable for this task when the rating data is sparse. This paper presents a novel approach, based on the SimRank algorithm, to compute similarity values when ratings are limited. Unlike correlation-based methods, which only consider user ratings for common items, this approach uses all the available ratings, allowing it to compute meaningful similarities. To evaluate the usefulness of this approach, we test it on the problem of predicting the ratings of users for movies and jokes.