The week's pick
Reseach Article
Improved Collaborative Filtering using Evolutionary Algorithm based Feature Extraction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 64 - Number 20 |
| Year of Publication: 2013 |
| Authors: Deepa Anand |
10.5120/10750-5701
|
Deepa Anand . Improved Collaborative Filtering using Evolutionary Algorithm based Feature Extraction. International Journal of Computer Applications. 64, 20 ( February 2013), 20-26. DOI=10.5120/10750-5701
Abstract
The ubiquity of Collaborative Filtering systems is evident in the wide variety of domains to which they have been applied successfully. However a major challenge to such systems is the high dimensionality and sparsity of the expressed preferences. Dealing effectively with large user profiles would improve the scalability of the system whereas reducing sparsity would increase the quality of recommendations. Several approaches in this direction have focused on feature selection and feature extraction in order to reduce the data dimension and thus make the recommendation process more scalable. Some of the features extraction techniques are based on extracting content based features. However many such solutions have been handcrafted and thus not guaranteed to work optimally under all data environments. This work explores Evolutionary algorithms based feature extraction techniques where the extracted features may describe user or item profiles. The features constructed/extracted thus are compact, dense and are discriminative. Moreover they have the advantage of requiring no extra information (such as content description) and are adaptive, delivering the optimal feature extraction scheme for the particular dataset. We have performed experiments with the popular MovieLens dataset and have compared the user-based and item-based evolutionary feature extraction schemes with respect to their accuracy. The experiments establish that the evolutionary feature extraction schemes score over traditional algorithms as well as content-based feature extraction schemes. Moreover we find that the item-based evolutionary feature extraction schemes outperform their user-based counterparts under varying parameter values.
References
- Adomavicius, G. , Tuzhilin, A. 2005/ Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, IEEE Trans. on Knowl. and Data En. , vol. 17(6), pp. 734–749, doi:10. 1109/TKDE. 2005. 99.
- Alba, E. , Chicano, J. 2004. Training neural networks with GA hybrid algorithms in Proceedings of Conference on Genetic and Evolutionary Computation–GECCO, pp. 852-863
- Anand, D. , Bharadwaj, K. K. 2010. Enhancing Accuracy of Recommender System through Adaptive Similarity Measures based on Hybrid Features,in Proceedings 2nd Asian Conference on Intelligent Information and Database Systems(ACIIDS 2010), LNAI 5991, pp. 1-10,
- Anand, D. , Bharadwaj, K. K. 2012. Pruning Trust-Distrust network via Reliability and Risk Estimates for Quality Recommendations, Social Network Analysis and Mining, Springer.
- Anand, D. , Bharadwaj, K. K. 2012. Exploring Graph Based Global Similarity Estimates for Quality Recommendations, International Journal of Computational Science and Engineering, Inderscience(Accepted for publication)
- Baltrunas, L. , Ricci, F. 2007. Dynamic Item Weighting and Selection for Collaborative Filtering, In Berendt, B. , Mladenic, D. , Semeraro, G. , Spiliopoulou, M. , and Stumme, G. , editors, Web Mining 2. 0, International Workshop Located at the ECML/PKDD 200, Warsaw, Poland.
- Symeonidis, P. 2007. Content-based Dimensionality Reduction for Recommender Systems, in Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation, pp. 619-626
- Resnick, P. , Iacovou, N. , Suchak, M. , Bergstrom, P. , & Riedl , J. 1994. Grouplens An open architecture for collaborative filtering of netnews. In Proceedings of ACM CSCW'94 conference on computer-supported cooperative work, pp. 175–186.
- Pei1, M. Goodman, E. D. , Punch, W. F. 1997. Feature Extraction Using Genetic Algorithms, in Proceeding of International Symposium on Intelligent Data Engineering and Learning'98 (IDEAL'98), Hong Kong
- Sarwar, B. , Karypis, G. , Konstan, J. , Riedl, J. 2000. Application of Dimensionality Reduction in Recommender Systems—A Case Study, in Proceedings of ACM WebKDD Workshop.
- Ciesielczyk, M. , Szwabe, A. 2011. RSVD-based Dimensionality Reduction for Recommender Systems, International Journal of Machine Learning and Computing, Vol. 1, No. 2/
- Felfernig, A. Friedrich, G. , Schmidt-Thieme, L. , 2007. Guest Editors' Introduction: Recommender Systems, IEEE Intelligent Systems, v. 22 n. 3, p. 18-21, [doi: 10. 1109/MIS. 2007. 52]
- Ding, S. , Zhao, S. Yuan, Q. , Zhang,X. , Fu, P. , Bergman, L. 2008. Boosting collaborative filtering based on statistical prediction errors, in Proceedings of the ACM conference on Recommender systems, Lausanne, Switzerland [doi>10. 1145/1454008. 1454011]
- O'Donovan, J. , Smyth, B. 2005. Trust in recommender systems. in: Proceedings 10th international conference on Intelligent user interfaces(IUI '05), ACM, pp. 167–174.
- Anand, D. : 2012. Evolutionary Algorithm based Feature Extraction for Enhanced Recommendations, in Proceedings of ARTCom 2012, LNEE pp. 410–413.
- Lerner, B. , Guterman, H. , Aladjem, M. , Dinstein, I. . 1996. Feature Extraction by Neural Network Nonlinear Mapping for Pattern Classification, in Proceedings of the 13th International Conference on Pattern Recognition, ICPR13, Vienna, vol. 4, 320-324.
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