Clustering Algorithms in Hybrid Recommender System on MovieLens Data
Languages of publication
Decisions are taken by humans very often during professional as well as leisure activities. It is particularly evident during surfing the Internet: selecting web sites to explore, choosing needed information in search engine results or deciding which product to buy in an on-line store. Recommender systems are electronic applications, the aim of which is to support humans in this decision making process. They are widely used in many applications: adaptive WWW servers, e-learning, music and video preferences, internet stores etc. In on-line solutions, such as e-shops or libraries, the aim of recommendations is to show customers the products which they are probably interested in. As input data the following are taken: shopping basket archives, ratings of the products or servers log files. The article presents a solution of recommender system which helps users to select an interesting product. The system analyses data from other customers' ratings of the products. It uses clustering methods to find similarities among the users and proposed techniques to identify users' profiles. The system was implemented in Apache Mahout environment and tested on a movie database. Selected similarity measures are based on: Euclidean distance, cosine as well as correlation coefficient and loglikehood function.
- Agarwal, N., Haque, E., Liu, H., & Parsons, L. (2005). Research paper recommender systems: A subspace clustering approach. In Advances in Web-Age Information Management (pp. 475–491). Springer.
- Anand, S. S., & Mobasher, B. (2003). Intelligent techniques for web personalization. In Proceedings of the 2003 International Conference on Intelligent Techniques for Web Personalization (pp. 1–36). Springer-Verlag.
- Bridge, D., & Kelleher, J. (2002). Experiments in sparsity reduction: Using clustering in collaborative recommenders. In Artificial Intelligence and Cognitive Science (pp. 144–149). Springer.
- Cremonesi, P., Donatacci, A., Garzotto, F., & Turrin, R. (2012). Decision-Making in Recommender Systems: The Role of User's Goals and Bounded Resources. In 6th ACM Conference on Recommender Systems (pp. 1–7).
- Gavalas, K. M. D. (2011). A web-based pervasive recommendation system for mobile tourist guides. In Personal and Ubiquitous Computing (Vol. 15, pp. 759–770). Springer-Verlag.
- Haruechaiyasak, C., Tipnoe, C., Kongyoung, S., Damrongrat, C., & Angkawattanawit, N. (2005). A dynamic framework for maintaining customer profiles in e-commerce recommender systems. In IEEE International Conference on e-Technology, e-Commerce and e-Service, (pp. 768–771).
- Jain, A. K., Murty, M. N., & Flynn, P. J. (1999) Data clustering: a review. ACM Computing Surveys, 31(3), 264–323.[Crossref]
- Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: an introduction, Cambridge University Press.
- Kantor, P. B., Ricci, F., Rokach, L., & Shapira, B. (2011). Recommender systems handbook. Springer.[WoS]
- Kim, Y. S., T.H. (2005). An Effective Recommendation Algorithm for Clustering-Based Recommender Systems. In Lecture Notes in Artificial Intelligence (Vol. 3809, p. 1150–1153). Springer-Verlag.
- Kużelewska, U. (2013). Advantages of Information Granulation in Clustering Algorithms. In Agents and Artificial Intelligence (pp. 131–145). Springer.
- Li, L., Wang, D.-D., Zhu, S.-Z., & Li, T. (2011). Personalized news recommendation: a review and an experimental investigation. Journal of Computer Science and Technology, 26(5), 754–766.[Crossref][WoS]
- Mahmood, T., & Ricci, F. (2009). Improving recommender systems with adaptive conversational strategies. In Proceedings of the 20th ACM Conference on Hypertext and Hypermedia (pp. 73–82).
- Malak, A.-H., Yan, L. H., & Jie, L. (2011). Personalized e-Government Services: Tourism Recommender System Framework. In Lecture Notes in Business Information Processing, (Vol. 75, p. 173–187). Springer.
- Moghaddam, S. G., & Selamat, A. (2011). A scalable collaborative recommender algorithm based on user density-based clustering. In 3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA) (pp. 246–249).
- MovieLens 100k Data Set. (n.d). Retrieved 01.02.2013, from http://www.grouplens.org/node/73.
- Pitsilis, G. e. a. (2011). Clustering Recommenders in Collaborative Filtering Using Explicit Trust Information. In Trust Management V (Vol. 358, p. 82–97). Springer.
- Rongfei, J., Maozhong, J., & Chao, L. (2010). A new clustering method for collaborative filtering. In International Conference on Networking and Information Technology (pp. 488–492).
- Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web (pp. 285–295).
- Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2002). Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. In Proceedings of the 5th International Conference on Computer and Information Technology (Vol. 1).
- Schiaffino, A. A., S. (2009). Building an expert travel agent as a software agent. In Expert Systems with Applications (Vol. 36, pp. 1291–1299). Elsevier.
Publication order reference