Feature Augmentation based Hybrid Collaborative Filtering using Tree Boosted Ensemble

Udayabalan Balasingam, Gopalan Palaniswamy

Abstract


Text of the abstract: Requirements for recommendation systems are currently on the raise due to the huge information content available online and the inability of users to manually filter required data. This paper proposes a Feature augmentation based hybrid collaborative filtering using Tree Boosted Ensemble (TBE), for prediction. The proposed TBE recommender is formulated in two phases. The first phase creates category based training matrix using similar user profiles, while the second phase employs the boosted tree based model to predict ratings for the items. A threshold based filtering is finally applied to obtain precise recommendations for the user. Experiments were conducted with MovieLens dataset and performances were measured in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The proposed model was observed to exhibit MAE levels of 0.64 and RMSE levels of 0.77 with a variation level of ±0.1. Comparisons with state-of-the-art models indicate that the proposed TBE model exhibits reductions in MAE at 6% to 14% and RMSE at ~0.2.

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References


Vickery, A., and Vickery, B. C. (2005). Information science in theory and practice. De Gruyter

Ricci, F., Rokach, L., and Shapira, B. (2011). Introduction to recommender systems handbook Boston, MA: Springer US . pp. 1-5.

Karlgren, Jussi. "A digital bookshelf: original work on recommender systems"

Lohr, S. (2009). A $1 million research bargain for Netflix, and maybe a model for others. The New York Times, 22.

Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17 (6), 734–749.

Prem Melville and Vikas Sindhwani, (2010) Recommender Systems, Encyclopedia of Machine Learning, pp. 1-9.

Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002). Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 253-260

Sarwar, B. M., Karypis, G., Konstan, J., & Riedl, J. (2002). Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. In Proceedings of the fifth international conference on computer and information technology, Vol. 1.

Mooney, R.J., and Roy, L. (1999). Content-based book recommendation using learning for text categorization. In Workshop Recom. Sys.: Algo. and Evaluation.

Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: an introduction. Cambridge University Press.

Paradarami, T.K., Bastian, N.D. and Wightman, J.L., (2017). A hybrid recommender system using artificial neural networks. Expert Systems with Applications, vol. 83, pp.300-313.

Chen, W., Niu, Z., Zhao, X., and Li, Y. (2014). A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web, doi: 10. 1007/s11280-012-0187-z .vol. 17(2), pp.271-284.

Dooms, S., Pessemier, T., and Martens, L. (2015). Offline optimization for user-specific hybrid recommender systems. Multimedia Tools and Applications, doi: 10.1007/s11042- 013- 1768- 2, vol. 74(9) , pp.3053-3076.

Wu, M.L., Chang, C.H., and Liu, R.Z. (2014). Integrating content-based filtering with collaborative filtering using co-clustering with augmented matrices. Expert Systems with Applications, doi: 10.1016/j.eswa.2013.10.008 vol. 41(6), pp.2754-2761.

Zheng, Y. (2014). Semi-supervised context-aware matrix factorization: Using contexts in a way of “latent” factors. In Proceedings of the 29th annual ACM symposium on applied computing . In SAC New York, NY, USA: ACM. doi: 10.1145/2554850.2555195 .vol.14, pp.292-293.

Katarya, R. and Verma, O.P., (2016). An effective collaborative movie recommender system with cuckoo search. Egyptian Informatics Journal.

Banati, H. and Mehta, S., (2010). A Multi-Perspective Evaluation of ma and ga for collaborative Filtering Recommender System. International journal of computer science & information Technology (IJCSIT), vol. 2(5), pp.103-122.

Alam, S., Dobbie, G. and Riddle, P., (2011), May. Towards recommender system using particle swarm optimization based web usage clustering. In Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer, Berlin, Heidelberg.pp.316-326.

Nadi, S., Saraee, M., Bagheri, A. and Davarpanh Jazi, M., (2011). FARS: Fuzzy ant based recommender system for web users. International Journal of Computer Science Issues, vol.8 (1), pp.203-209.

Frémal, S. and Lecron, F., (2017). Weighting strategies for a recommender system using item clustering based on genres. Expert Systems with Applications, vol.77, pp.105-113.

Ma, X., Lu, H., Gan, Z., and Zhao, Q. (2016). An exploration of improving prediction accuracy by constructing a multi-type clustering based recommendation framework. Neurocomputing, vol.191, pp. 388–397.

Guo, G., Zhang, J., and Yorke-Smith, N. (2015). Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems. Knowledge-Based Systems, vol. 74 , pp.14–27 .

McAuley, J., Pandey, R., and Leskovec, J. (2015).a Inferring networks of substitutable and complementary products. In Proceedings of the twenty first ACM SIGKDD international conference on knowledge discovery and data mining . In KDD15 New York, NY, USA: ACM. doi: 10.1145/2783258.2783381 .pp.785-794.

Kunaver, M., & Požrl, T. (2017). Diversity in recommender systems–A survey. Knowledge-Based Systems, Vol. 123, pp. 154-162.

Sorensen, S. (2012). Accuracy of similarity measures in recommender systems. In Proceedings of the 29th SemiAnnual Computer Science Senior Seminar Conference (Minnesota, USA.

Rokach, L. (2010). "Ensemble-based classifiers". Artificial Intelligence Review. Vol. 33, pp. 1–39.

Breiman, L. (1997) "Arcing The Edge"

https://grouplens.org/datasets/movielens/

Harper, F.M., and Konstan, J.A. (2015). The MovieLens datasets: History and context. ACM Transactions on Interactive Intelligent Systems, doi: 10.1145/2827872 .vol. 5(4), pp.19:1-19:19.

Dooms, S., De Pessemier, T., & Martens, L. (2015). Offline optimization for user-specific hybrid recommender systems. Multimedia Tools and Applications, 74(9), 3053-3076.

Ge, X., Liu, J., Qi, Q., & Chen, Z. (2011). A new prediction approach based on linear regression for collaborative filtering. IEEE Eighth International Conference In Fuzzy Systems and Knowledge Discovery (FSKD), Vol. 4, pp. 2586-2590




DOI: https://doi.org/10.31449/inf.v43i4.2141

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