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Item-based collaborative filtering recommendation algorithms
This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
This work presents a new coarsening heuristic (called heavy-edge heuristic) for which the size of the partition of the coarse graph is within a small factor of theSize of the final partition obtained after multilevel refinement, and presents a much faster variation of the Kernighan--Lin (KL) algorithm for refining during uncoarsening.
A Comparison of Document Clustering Techniques
This paper compares the two main approaches to document clustering, agglomerative hierarchical clustering and K-means, and indicates that the bisecting K-MEans technique is better than the standard K-Means approach and as good or better as the hierarchical approaches that were tested for a variety of cluster evaluation metrics.
Item-based top-N recommendation algorithms
This article presents one class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended, and shows that these item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality.
Multilevel k-way Partitioning Scheme for Irregular Graphs
In this paper, we present and study a class of graph partitioning algorithms that reduces the size of the graph by collapsing vertices and edges, we find ak-way partitioning of the smaller graph, and…
Analysis of recommendation algorithms for e-commerce
This paper investigates several te hniques for analyzing large-s ale pur hase and preferen e data for the purpose of producing useful re ommendations to ustomers and devise and apply their ombinations on the authors' data sets to ompare for re Ommendation quality and performan e.
Frequent subgraph discovery
- M. Kuramochi, G. Karypis
- Computer ScienceProceedings IEEE International Conference on…
- 29 November 2001
The empirical results show that the algorithm scales linearly with the number of input transactions and it is able to discover frequent subgraphs from a set of graph transactions reasonably fast, even though it has to deal with computationally hard problems such as canonical labeling of graphs and subgraph isomorphism which are not necessary for traditional frequent itemset discovery.
Application of Dimensionality Reduction in Recommender System - A Case Study
This paper presents two different experiments where one technology called Singular Value Decomposition (SVD) is explored to reduce the dimensionality of recommender system databases and suggests that SVD has the potential to meet many of the challenges ofRecommender systems, under certain conditions.
SLIM: Sparse Linear Methods for Top-N Recommender Systems
- Xia Ning, G. Karypis
- Computer ScienceIEEE 11th International Conference on Data Mining
- 11 December 2011
A novel Sparse Linear Method (SLIM) is proposed, which generates top-N recommendations by aggregating from user purchase/rating profiles and a sparse aggregation coefficient matrix W is learned from SLIM by solving an `1-norm and `2-norm regularized optimization problem.
Chameleon: Hierarchical Clustering Using Dynamic Modeling
Chameleon's key feature is that it accounts for both interconnectivity and closeness in identifying the most similar pair of clusters, which is important for dealing with highly variable clusters.