Abstract
Recommender systems use machine learning and data mining techniques to filter unseen information and predict whether a user would like a particular item. A major research challenge in this field is to make useful recommendation from available set of millions of items with sparse ratings. A large number of approaches have been proposed aiming to increase accuracy, but they have ignored potential problems, such as sparsity and cold start problems. From this line of research, in this research work, we have proposed a novel hybrid recommendation framework that combines content-based filtering with collaborative filtering that overcome aforementioned problems. Our experimental results show that this performance of proposed algorithm is better or comparable with the individual content-based approaches and naive hybrid approaches, while it eliminates various problems faced by recommender systems.
Similar content being viewed by others
References
Alsalama, A.: A hybrid recommendation system based on association rules. Masters Thesis and Specialist Project Faculty of the Department of Computer Science, Western Kentucky University (2015)
Badaro, G.; Hajj, H.; El-Hajj, W.; Nachman, L.: A hybrid approach with collaborative filtering for recommender systems. In: Wireless Communications and Mobile Computing Conference (IWCMC), 2013 9th International (pp. 349–354). IEEE (2013)
Balabanović, M.; Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40, 66–72 (1997)
Basu, C.; Hirsh, H.; Cohen, W. et al.: Recommendation as classification: using social and content-based information in recommendation. In: AAAI/IAAI (pp. 714–720) (1998)
Bennett, J.; Lanning, S.: The netflix prize. In: Proceedings of KDD Cup and Workshop (p. 35). vol. 2007 (2007)
Bhargava, N.; Sharma, G.; Bhargava, R.; Mathuria, M.: Decision tree analysis on j48 algorithm for data mining. In: Proceedings of International Journal of Advanced Research in Computer Science and Software Engineering, 3 (2013)
Billsus, D.; Pazzani, M.J.: User modeling for adaptive news access. User Model. User-Adapt. Interact. 10, 147–180 (2000)
Breese, J.S.; Heckerman, D.; Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence (pp. 43–52). Morgan Kaufmann Publishers Inc. (1998)
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adapt. Interact. 12, 331–370 (2002)
Burke, R.: Hybrid web recommender systems. In: The Adaptive Web (pp. 377–408). Springer, Berlin (2007)
Carrer-Neto, W.; Hernández-Alcaraz, M.L.; Valencia-García, R.; García-Sánchez, F.: Social knowledge-based recommender system: application to the movies domain. Expert Syst. Appl. 39, 10990–11000 (2012)
Cotter, P., Smith, B.: Ptv: Intelligent personalised tv guides. In: AAAI/IAAI (pp. 957–964) (2000)
Dooms, S.; Audenaert, P.; Fostier, J.; De Pessemier, T.; Martens, L.: In-memory, distributed content-based recommender system. J. Intell. Inf. Syst. 42, 645–669 (2014)
Ekstrand, M.D.; Kannan, P.; Stemper, J.A.; Butler, J.T.; Konstan, J.A.; Riedl, J.T.: Automatically building research reading lists. In: Proceedings of the Fourth ACM Conference on Recommender Systems (pp. 159–166). ACM (2010)
Ekstrand, M.D.; Riedl, J.T.; Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum.-Comput. Interact. 4, 81–173 (2011)
Fang, B.; Liao, S.; Xu, K.; Cheng, H.; Zhu, C.; Chen, H.: A novel mobile recommender system for indoor shopping. Expert Syst. Appl. 39, 11992–12000 (2012)
Ferrara, F.; Pudota, N.; Tasso, C.: A keyphrase-based paper recommender system. In: Digital Libraries and Archives (pp. 14–25). Springer, Berlin (2011)
Ghazanfar, M.; Prugel-Bennett, A.: Building switching hybrid recommender system using machine learning classifiers and collaborative filtering. IAENG Int. J. Comput. Sci. 37 (2010)
Ghazanfar, M.A.: Robust, scalable, and practical algorithms for recommender systems. Ph.D. thesis University of Southampton (2012)
Ghazanfar, M.A.; Prugel-Bennett, A.: A scalable, accurate hybrid recommender system. In: Knowledge Discovery and Data Mining, 2010. WKDD’10. Third International Conference on (pp. 94–98). IEEE (2010)
Ghazanfar, M.A.; Prügel-Bennett, A.: The advantage of careful imputation sources in sparse data-environment of recommender systems: generating improved SVD-based recommendations. Informatica 13, 61–92 (2013)
Ghazanfar, M.A.; Prügel-Bennett, A.: Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems. Expert Syst. Appl. 41, 3261–3275 (2014)
Ghazanfar, M.A.; Prügel-Bennett, A.; Szedmak, S.: Kernel-mapping recommender system algorithms. Inf. Sci. 208, 81–104 (2012)
Good, N.; Schafer, J.B.; Konstan, J.A.; Borchers, A.; Sarwar, B.; Herlocker, J.; Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: AAAI/IAAI (pp. 439–446) (1999)
Grasso, A.; Convertino, G.: Collective intelligence in organizations: tools and studies. In: Computer Supported Cooperative Work (CSCW), (pp. 1–13) (2012)
Herlocker, J.L.; Konstan, J.A.; Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work (pp. 241–250). ACM (2000)
Herlocker, J.L.; Konstan, J.A.; Terveen, L.G.; Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22, 5–53 (2004)
Isinkaye, F.; Folajimi, Y.; Ojokoh, B.: Recommendation systems: principles, methods and evaluation. Egypt. Inform. J. 16, 261–273 (2015)
Jack, K.: Mendeley: recommendation systems for academic literature. Presentation at Technical University of Graz (TUG) (2012)
Jiang, Y.; Jia, A.; Feng, Y.; Zhao, D.: Recommending academic papers via users’ reading purposes. In: Proceedings of the Sixth ACM Conference on Recommender Systems (pp. 241–244). ACM (2012)
Kużelewska, U.: Advantages of information granulation in clustering algorithms. In: Agents and Artificial Intelligence (pp. 131–145). Springer, Berlin (2011)
Lucas, J.P.; Luz, N.; Moreno, M.N.; Anacleto, R.; Figueiredo, A.A.; Martins, C.: A hybrid recommendation approach for a tourism system. Expert Syst. Appl. 40, 3532–3550 (2013)
Mathew, S.K.: Adoption of business intelligence systems in indian fashion retail. Int. J. Bus. Inf. Syst. 9, 261–277 (2012)
Melville, P.; Mooney, R.J.; Nagarajan, R.: Content-boosted collaborative filtering (2009)
Middleton, S.E.; Shadbolt, N.R.; De Roure, D.C.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. (TOIS) 22, 54–88 (2004)
Mobasher, B.: Recommender systems. KI 21, 41–43 (2007)
Mohanraj, V.; Chandrasekaran, M.; Senthilkumar, J.; Arumugam, S.; Suresh, Y.: Ontology driven bee’s foraging approach based self adaptive online recommendation system. J. Syst. Softw. 85, 2439–2450 (2012)
Mooney, R.J.; Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital Libraries (pp. 195–204). ACM (2000)
Park, S.-T., Pennock, D., Madani, O., Good, N., DeCoste, D.: Naïve filterbots for robust cold-start recommendations. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 699–705). ACM (2006)
Pazzani, M.: A framework for collaborative, content-based and demographic filtering. Department of Information and Computer Science. University of California, Irvine. Irvine, CA, 92697 (1999)
Resnick, P.; Iacovou, N.; Suchak, M.; Bergstrom, P.; Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (pp. 175–186). ACM (1994)
Salehi, M.; Nakhai Kamalabadi, I.: A hybrid recommendation approach based on attributes of products using genetic algorithm and naive bayes classifier. Int. J. Bus. Inf. Syst. 13, 381–399 (2013)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of the 2nd ACM Conference on Electronic Commerce (pp. 158–167). ACM (2000)
Sarwar, B.; Karypis, G.; Konstan, J.; Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web (pp. 285–295). ACM (2001)
Sarwar, B.M.; Konstan, J.A.; Borchers, A.; Herlocker, J.; Miller, B.; Riedl, J.: Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In: Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work (pp. 345–354). ACM (1998)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. (CSUR) 34, 1–47 (2002)
Sharif, M.A., Raghavan, V.V.: A large-scale, hybrid approach for recommending pages based on previous user click pattern and content. In: Foundations of Intelligent Systems (pp. 103–112). Springer, Berlin (2014)
Son, L.H.: HU-FCF: a hybrid user-based fuzzy collaborative filtering method in recommender systems. Expert Syst. Appl.: Int. J. 41, 6861–6870 (2014)
Tsai, C.-F.; Hsu, Y.-F.; Lin, C.-Y.; Lin, W.-Y.: Intrusion detection by machine learning: a review. Expert Syst. Appl. 36, 11994–12000 (2009)
Zarrinkalam, F.; Kahani, M.: Semcir: a citation recommendation system based on a novel semantic distance measure. Program 47, 92–112 (2013)
Zhang, T.; Iyengar, V.S.: Recommender systems using linear classifiers. J. Mach. Learn. Res. 2, 313–334 (2002)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sattar, A., Ghazanfar, M.A. & Iqbal, M. Building Accurate and Practical Recommender System Algorithms Using Machine Learning Classifier and Collaborative Filtering. Arab J Sci Eng 42, 3229–3247 (2017). https://doi.org/10.1007/s13369-016-2410-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-016-2410-1