Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
A new method for similarity indexing of market basket data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Hi-index | 0.00 |
Collaborative filtering is the most successful recommender system technology to date. It has been shown to produce high quality recommendations, but the performance degrades with the number of customers and products. In this paper, according to the feature of the rating data, we present a new similarity function Hsim(), and a signature table-based Algorithm for performing collaborative filtering. This method partitions the original data into sets of signature, then establishes a signature table to avoid a sequential scan. Our preliminary experiments based on a number of real data sets show that the new method can both improve the scalability and quality of collaborative filtering. Because the new method applies data clustering algorithms to rating data, predictions can be computed independently within one or a few partitions. Ideally, partition will improve the quality of collaborative filtering predictions. We'll continue to study how to further improve the quality of predictions in the future research.