Performance Comparison of Collaborative Filtering Prediction Methods on Recommendation System

  • Win Win Moe Department of Computer Engineering and Information Technology, Mandalay Technological University, Mandalay, Myanmar
  • Nang Aye Aye Htwe Department of Computer Engineering and Information Technology, Mandalay Technological University, Mandalay, Myanmar
Keywords: Collaborative filtering, Ensemble learning, Prediction algorithm, Recommendation system, Similarity measure.

Abstract

Recommendation systems were introduced as the computer-based intelligent techniques to deal with the problem of information overload. Collaborative filtering is a simple recommendation algorithm that executes the similarity (neighborhoods) between items and then computes the missing data predictions. A serious limitation of collaborative filtering is the sparisity problem, referring to the situation where insufficient rating history is available for inferring reliable similarities. This research compares four prediction methods: Weighted Sum, Mean-Centering, Boosted Weighted Sum and Boosted Double Means Centering predictions. Boosting double means centering taken into account information of both users and items in order to overcome the potential decrease of accuracy due to sparsity when predicting the missing value. It tries to capture the user and item biases from the whole effects so as to enable the better concentrating on user-item interaction. Furthermore, ensemble learning will improve the performance collaborative filtering method because an ensemble of collaborative filtering models based on a single collaborative filtering algorithm considered the problem of sparsity, recommender error rate and sample weight update. Rating history in Book-Crossing dataset with 91% sparsity level is used to evaluate the missing rating predictions and the performance comparison of rating predictions on two traditional collaborative filtering and two boosting collaborative filtering frameworks. Experimental results shows that the proposed boosted double mean centering framework improve the prediction accuracy than the two traditional collaborative filtering and the other boosting prediction algorithm.

References

[1] Greg Linden, Brent Smith, and Jeremy York, “Recommendations Item-to-Item Collaborative Filtering”, IEEE INTERNET COMPUTING, JANUARY-FEBUARY, 2003.
[2] Claudio Adrian Levinas, “An Analysis of Memory Based Collaborative Filtering Recommender Systems with Improvement Proposals”, September 2014.
[3] Alon Schclar, Alexander Tsikinovsky and Lior Rokach, “Ensemble Methods for Improving the Performance of Neighborhood-based Collaborative Filtering”, October 23-25, 2009, New York, USA.
[4] Badrul Sarwar, George Karypis, Joseph Konstan and John Riedl, “Item-Based Collaborative Filtering Recommendation Algorithms”, In Proceedings of the 10th international conference on World Wide Web, pages 285-295. ACM, 2001.
[5] Emmanouil Vozalis and Konstantinos G. Margaritis, “Analysis of Recommender Systems' Algorithms”, http://macedonia.uom.gr.
[6] Xiaotian Jiang and et al “Novel Boosting Frameworks to Improve the Performance of Collaborative Filtering”, JMLR: Workshop and Conference Proceedings 29:87-99, 2013.
[7] Y. Freund and R. E. Schapire, “Experiments with a New Boosting Algorithm”, Machine Learning: Proceedings of the Thirteenth International Conference, 1996.
[8] R. Sahal, S. Selim and A. Eikorany, “An Adaptive Framework for Enhancing Recommendation using Hybrid Techniques”, International Journal of Computer Science & Information Technology (IJCSIT) Vol 6, No 2, April, 2014.
[9] A. Bellogin and A. P. Vries, “Understanding Similarity Metrics in Neighbour-based Recommender Systems”.
[10] X. Su and T. M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques”, Advance in Artificial Intelligence, 2009.
[11] P. Adamopoulos and A. Tuzhilin, “On Over-Specialization and Concentration Bias of Recommendation: Probabilistic Neighborhood Selection in Collaborative Filtering Systems”, 2014.
[12] C. Lili, “Recommender Algorithms based on Boosting Ensemble Learning”, International Journal on Smart Sensing and Intelligent System Vol 8, No 1, March 2015.
Published
2017-02-02
Section
Articles