NINU: An Incremental User-Based Algorithm for Data Sparsity Recommender Systems

Yang Zhang; Hua Shen; Guo Shun Zhou

doi:10.4028/www.scientific.net/AMM.201-202.428

Paper Titles

Current Analysis of Power Devices of the Sub-Module in New Modular Multi-Level Converter
p.410

A Watermark Algorithm Based on DCT and M Sequence Scrambling
p.414

Aviation Ad Hoc Network Multipath Routing Protocol of Path Segmentation Based
p.418

A Driver Model for Direction Control in Intelligent Driving Based on Fuzzy Control
p.422

NINU: An Incremental User-Based Algorithm for Data Sparsity Recommender Systems
p.428

Regenerative Braking System Pressure Control Calculation Based on ABS Hydraulic Model
p.433

Exploring the Effect of Length and Angle on NACA 0010 for Diffuser Design in Tidal Current Turbines
p.438

PLC Control System of the Stage Loader Based on Step Ladder Instruction
p.442

The Application and Parameter Optimization of Pascal Curve Gear in the Transplanting Mechanism
p.446

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 201-202NINU: An Incremental User-Based Algorithm for Data...

NINU: An Incremental User-Based Algorithm for Data Sparsity Recommender Systems

Abstract:

Collaborative Filtering (CF) algorithms are widely used in recommender systems to deal with information overload. However, with the rapid growth in the amount of information and the number of visitors to web sites in recent years, CF researchers are facing challenges with improving the quality of recommendations for users with sparse data and improving the scalability of the CF algorithms. To address these issues, an incremental user-based algorithm combined with item-based approach is proposed in this paper. By using N-nearest users and N-nearest items in the prediction generation, the algorithm requires an O(N) space for storing necessary similarities for the online prediction computation and at the same time gets improvement of scalability. The experiments suggest that the incremental user-based algorithm provides better quality than the best available classic Pearson correlation-based CF algorithms when the data set is sparse.

You might also be interested in these eBooks

Advances in Engineering Design and Optimization III

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 201-202)

Pages:

428-432

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.201-202.428

Citation:

Cite this paper

Online since:

October 2012

Authors:

Yang Zhang, Hua Shen, Guo Shun Zhou

Keywords:

Collaborative Filtering (CF), Data Sparsity, Recommender System, User-Based

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] M. R. McLaughlin and J. L. Herlocker: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Sheffield, UK, 2004), pp.329-336.

Google Scholar

[2] B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl: Proceedings of the 10th International Conference on World Wide Web (Hong Kong, May 1-5, 2001), pp.285-295.

DOI: 10.1145/371920.372071

Google Scholar

[3] Z. Huang, H. Chen, and D. Zeng: ACM Transactions on Information Systems, Vol. 22 (2004) No. 1, p.116–142.

Google Scholar

[4] J. Canny: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Tampere, Finland, August, 2002), p.238–245.

Google Scholar

[5] C.-N. Ziegler, G. Lausen, and L. Schmidt-Thieme: Proceedings of the 13th International Conference on Information and Knowledge Management (Washington, DC, USA, November, 2004), p.406–415.

Google Scholar