Farhan Ullah
ABSTRACT
In the modern world, people face an explosion of information and difficulty finding the right choice for their interests. Nowadays, people prefer online shopping for their needs. Recently, the recommender system has become one of the key technology for the online purchasing system. The collaborative filtering technique has been extensively applied for the Recommender Systems. However, collaborative filtering is suffering from data sparsity, cold start problems, and inaccuracy problems. To overcome these problems, we propose a novel approach of the Matrix Distributive collaborative filtering with ensemble integration. The experimental results illustrate the increase in performance against the existing methods.
- Y. Koren and R. Bell, "Advances in collaborative filtering", in Recommender Systems Handbook, Second Edition, 2015.Google Scholar
Cross Ref
- X. Wang, X. He, M. Wang, F. Feng, and T. S. Chua, "Neural graph collaborative filtering", in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 165--174, 2019.Google ScholarDigital Library
- S. Ghazarian and M. A. Nematbakhsh, "Enhancing memory-based collaborative filtering for group recommender systems", Expert Syst. Appl., vol. 42, no. 7, pp. 3801--3812, 2015.Google ScholarDigital Library
- S. Zhang, L. Yao, A. Sun, and Y. Tay, "Deep learning based recommender system: A survey and new perspectives", ACM Computing Surveys. vol. 52, no. 1, pp.38, 2019.Google ScholarDigital Library
- C. J. Willmott and K. Matsuura, "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance", Clim. Res., vol. 30, no. 1, pp. 79--82, 2005.Google ScholarCross Ref
- J. Bennett and S. Lanning, "The Netflix Prize", in Proceedings of KDD Cup and Workshop, 2007.Google Scholar
- Netflix, "Netflix Prize", Proceedings of KDD Cup and Workshop, 2009. [Online]. Available: www.netflixprize.com. [Accessed: 03-Jan-2019].Google Scholar
- Y. Koren, R. Bell, and C. Volinsky, "Matrix factorization techniques for recommender systems", vol. 42, no. 8, pp. 30--37, 2009.Google Scholar
- R. Kumar, B. K. Verma, and S. Sunder Rastogi, "Social Popularity based SVD++ Recommender System", Int. J. Comput. Appl., vol. 87, no. 14, pp. 33--37, 2014.Google ScholarCross Ref
- R. Burke, "Hybrid recommender systems: Survey and experiments", User Model. User-Adapted Interact., vol. 12, no. 4, pp. 331--370, 2002.Google ScholarDigital Library
- G. Takács, I. Pilászy, B. Németh, and D. Tikk, "Scalable Collaborative Filtering Approaches for Large Recommender Systems", J. Mach. Learn. Res., vol. 10, pp. 623--656, 2009.Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Analysis of recommendation algorithms for e-commerce", in Proceedings of the 2nd ACM conference on Electronic commerce - EC '00, pp. 158--167, 2000.Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms", in Proceedings of the 10th International conference on World Wide Web, pp. 285--295, 2001.Google ScholarDigital Library
- Y. Koren, "Factorization meets the neighborhood: a multifaceted collaborative filtering model", in Proceedings of the 14th ACM SIGKDD International conference on Knowledge Discovery and Data mining, pp. 426--434, 2008.Google ScholarDigital Library
- A. Hernando, J. Bobadilla, and F. Ortega, "A non negative matrix factorization for collaborative filtering recommender systems based on a bayesian probabilistic model," Knowl.-Based Syst., vol. 97, pp. 188--202, 2016.Google ScholarDigital Library
- R. M. Bell and Y. Koren, "Scalable collaborative filtering with jointly derived neighborhood interpolation weights", in Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 43--52, 2007.Google ScholarDigital Library
- Z. Zhang, Y. Liu and Z. Zhang, "Field-Aware Matrix Factorization for Recommender Systems," in IEEE Access, vol. 6, pp. 45690--45698, 2018.Google ScholarCross Ref
- R. Bell and Y. Koren, "Lessons from the Netflix Prize Challenge", SIGKDD Explorations 9, 2007.Google Scholar
- L. Candillier, F. Meyer, F. Fessant. "Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems". IEEE International Conference on Data Mining, pp. 242--255, 2008.Google Scholar
- X. Su, T.M Khoshgoftaar, X. Zhu, and R. Greiner. "Imputation-boosted collaborative filtering using machine learning classifiers". Symposium on Applied Computing, Proceedings of the 2008 ACM symposium on Applied computing, pp.949--950, 2008.Google ScholarDigital Library
- M. Wu "Collaborative Filtering via Ensembles of Matrix Factorizations". Proceedings of KDD Cup and Workshop, pp. 43--47, 2007.Google Scholar
- J.S Lee, J.-S., & S. Olafsson. "Two-way cooperative prediction for collaborative filtering recommendations". Expert Systems with Applications, vol. 36, no. 3, pp. 5353--5361, 2009.Google ScholarDigital Library
- A. Schclar, A. Meisels, A. Gershman, L. Rokach, A. Tsikinovsky. "Ensemble Methods for Improving the Performance of Neighborhood-based Collaborative Filtering". Proceedings of ACM RecSys pp. 261--264, 2009.Google Scholar
- M. Jahrer, A. Töscher, R. Legenstein, "Combining predictions for accurate recommender systems", Proc. 16th ACM SIGKDD, pp. 693--702, 2010.Google Scholar
- Q. Ho, J. Cipar, H. Cui, and S. Lee, "More effective distributed ML via a stale synchronous parallel parameter server," in Proceedings -Advances in Neural Information Processing Systems (NIPS), pp. 1223--1231, 2013.Google Scholar
- H. F. Yu, C. J. Hsieh, S. Si, and I. Dhillon, "Scalable coordinate descent approaches to parallel matrix factorization for recommender systems", in Proceedings - IEEE International Conference on Data Mining (ICDM), pp. 765--774, 2012.Google ScholarDigital Library
- Y. Zhuang, W.-S. Chin, Y.-C. Juan, and C.-J. Lin, "A fast parallel SGD for matrix factorization in shared memory systems", in Proceedings of the ACM conference on Recommender systems, pp. 249--256, 2013.Google ScholarDigital Library
- H. Li, K. Li, J. An, and K. Li, "MSGD: A Novel Matrix Factorization Approach for Large-Scale Collaborative Filtering Recommender Systems on GPUs", IEEE Trans. Parallel Distrib. Syst., vol. 29, no. 7, pp. 1530--1544, 2018.Google ScholarDigital Library
- W. Tan, S. Chang, L. Fong, C. Li, Z. Wang, and L. Cao, "Matrix Factorization on GPUs with Memory Optimization and Approximate Computing", in Proceedings of the International Conference on Parallel Processing, 2018.Google ScholarDigital Library
- X. Guan, C. Li and Y. Guan, "Matrix Factorization With Rating Completion: An Enhanced SVD Model for Collaborative Filtering Recommender Systems," in IEEE Access, vol. 5, pp. 27668--27678, 2017.Google ScholarCross Ref
- R. Gemulla, E. Nijkamp, P. J. Haas, and Y. Sismanis, "Large-scale matrix factorization with distributed stochastic gradient descent", in Proceedings of the 17th ACM SIGKDD International conference on Knowledge Discovery and Data mining - KDD '11, pp. 69--77, 2011.Google ScholarDigital Library
- F.Maxwell and J. A. Konstan, "The MovieLens Datasets: History and Context", ACM Trans. Intell. Syst. Technol., vol. 5, no. 4, pp. 19, 2015.Google Scholar
- Y. Koren, "Factor in the neighbors: Factor in the Neighbors: Scalable and Accurate Collaborative Filtering", ACM Trans. Knowl. Discov. Data, vol. 4, no. 1, pp. 1--24, 2010.Google ScholarDigital Library
- Y. Zhou, D. Wilkinson, R. Schreiber, and R. Pan, "Large-scale parallel collaborative filtering for the netflix prize", in Proceedings of the International conference on Algorithmic Aspects in Information and Management, pp. 337--348, 2008.Google ScholarDigital Library
Index Terms
- Large-scale Distributive Matrix Collaborative Filtering for Recommender System
Recommendations
Collaborative Filtering for Recommender Systems
CBD '14: Proceedings of the 2014 Second International Conference on Advanced Cloud and Big DataCollaborative filtering (CF) predicts user preferences in item selection based on the known user ratings of items. As one of the most common approach to recommender systems, CF has been proved to be effective for solving the information overload ...
A preprocessing matrix factorization on collaborative filtering based library book recommendation system
DSIT '18: Proceedings of the 2018 International Conference on Data Science and Information TechnologyNowadays, recommendation systems are widely used to recommend items to the users that are specific to their individual preferences and most appropriate. For this reason, many academic libraries try to establish an effectiveness and efficiency book ...
Recommending items to group of users using Matrix Factorization based Collaborative Filtering
Group recommender systems are becoming very popular in the social web owing to their ability to provide a set of recommendations to a group of users. Several group recommender systems have been proposed by extending traditional KNN based Collaborative ...
Comments