Introduction to algorithms
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative filtering and the generalized vector space model (poster session)
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Latent Class Models for Collaborative Filtering
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
Neighbor search with global geometry: a minimax message passing algorithm
Proceedings of the 24th international conference on Machine learning
Effective missing data prediction for collaborative filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Collaborative Filtering Framework Based on Both Local User Similarity and Global User Similarity
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Enhancing link-based similarity through the use of non-numerical labels and prior information
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
A novel approach to compute similarities and its application to item recommendation
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Expert Systems with Applications: An International Journal
Improving the speed and stability of the k-nearest neighbors method
Pattern Recognition Letters
Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Finding co-solvers on twitter, with a little help from linked data
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
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Collaborative filtering as a classical method of information retrieval has been widely used in helping people to deal with information overload. In this paper, we introduce the concept of local user similarity and global user similarity, based on surprisal-based vector similarity and the application of the concept of maximin distance in graph theory. Surprisal-based vector similarity expresses the relationship between any two users based on the quantities of information (called surprisal) contained in their ratings. Global user similarity defines two users being similar if they can be connected through their locally similar neighbors. Based on both of Local User Similarity and Global User Similarity, we develop a collaborative filtering framework called LS&GS. An empirical study using the MovieLens dataset shows that our proposed framework outperforms other state-of-the-art collaborative filtering algorithms.