Attribute Clustering Based Collaborative Filtering

Abstract:

The paper proposed an attribute clustering based collaborative filtering algorithm for recommendation. It utilizes similarity to filter out redundant attributes by feature selection. Then by incorporating K-Means clustering, it is able to effectively solve the rating scale problems existing in the traditional collaborative filtering recommendation algorithm. The algorithm is verified by real data sets. Experiments use location information for clustering the restaurant data. By integration of users rating on restaurant service and external impression the experiment study combined the collaborative filtering philosophy to provide recommendation service for users. Experimental results show that compared with the item rating based recommended algorithm, the algorithm has ideal recommended quality and improved accuracy, and then it has reduced the data sparsity.

• Periodical: Applied Mechanics and Materials (Volumes 513-517)
• Pages: 965-968
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.513-517.965
• Online since: February 2014
• Authors: Liu Mei Zhang*, Jian Feng Ma, Di Lu, Yi Chuan Wang
• Keywords: Attributes Clustering, Feature Selection, Recommendation Algorithm, Score Predicts

References

[1] DENG Ailin, ZHU YangYong, SHI Baile. A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction [J]. Journal of Software. 2003, 14(09).

[2] LIU Meilian， LIU Tongcun，ZHANG Feng. Recommendation Algorithm Based on Item Rating Prediction Using Bidirectional Association Rules. JOURNAL OF WUHAN UNIVERSITY OF TECHNOLOGY. 2011, 33(9).

[3] Blanca Vargas-Govea, Juan Gabriel González-Serna, Rafael Ponce-MedellÃn. Effects of relevant contextual features in the performance of a restaurant recommender system. In RecSys’11: Workshop on Context Aware Recommender Systems (CARS-2011), Chicago, IL, USA, October 23, (2011).

[4] Kim D, Yum B J. Collaborative filtering based on iterative principal component analysis [J]. Expert Systems with Applications, 2005, 28(4): 823-830.

DOI: 10.1016/j.eswa.2004.12.037

[5] Garcia I, Sebastia L, Onaindia E. On the design of individual and group recommender systems for tourism [J]. Expert Systems with Applications, 2011, 38(6): 7683-7692.

DOI: 10.1016/j.eswa.2010.12.143

[6] Vozalis M G, Margaritis K G. Using SVD and demographic data for the enhancement of generalized collaborative filtering [J]. Information Sciences, 2007, 177(15): 3017-3037.

DOI: 10.1016/j.ins.2007.02.036

[7] Hsu M H. A personalized English learning recommender system for ESL students[J]. Expert Systems with Applications, 2008, 34(1): 683-688.

DOI: 10.1016/j.eswa.2006.10.004

[8] García E, Romero C, Ventura S, et al. A collaborative educational association rule mining tool[J]. The Internet and Higher Education, 2011, 14(2): 77-88.

DOI: 10.1016/j.iheduc.2010.07.006