Total views : 89
Collaborative Recommender Systems using User-item’s Multiclass Co-clustering
Recommender Systems are playing very crucial and vital role in day today’s life. People are very active on e-commerce sites as they get whatever they want at home. Some financial recommender sites like Money Control are getting popular due to their variety of sectors. These systems actually work on basis of Collaborative filtering model and apply knowledge discovery techniques for live interaction with person. E-commerce sites also provide top-N recommendations to users when they log in to system based on their previous shopping or surfing or interests. Hence collaborative filtering is most used technique over the decade. Objectives: 1. To find meaningful subgroups, formulate the Multiclass Co-Clustering (MCoC) algorithm and propose an effective solution to it. 2. Applying traditional and easily adoptable CF algorithm to merge Top-N recommendation results. Methods/Analysis: Collaborative filtering methods have been applied to different data like Sensing and monitoring data, financial data, and Electronic commerce and web applications. Findings: Simplicity, efficiency and Classification accuracy are most important feature provided by CFA. It is more natural to provide recommendations based on correlated user groups but it’s not mandatory that one user must have interest in other things that are liked by people in group. Novelty /Improvement: This approach can be considered as an extension of traditional clustering CF models. Multiclass co-clustering technique proposes to generate top-N recommendations by maintaining user-item interaction matrix and making clusters by matrix factorization.
Collaborative Filtering, Information Retrieval, Multiclass Co-clustering, Recommender System, User Profile
- Babu MSP, Kumar BRS. An implementation of user-based collaborative filtering algorithm. International Journal of Computer Science and Information Technologies. 2011; 2:1283–6.
- Sarwar B, Karypis G, Konstan J, Riedl J. Item based collaborative filtering recommendation algorithms. Group Lens Research Group/Army HPC Research Center; 2001. p.285–95. Crossref.
- Xu B, Bu JJ, Chen C. Improving collaborative recommender systems via user-item subgroups. IEEE Transactions on Knowledge and Data Engineering. 2016; 28:2363–74.Crossref.
- Kale P, Patil MR. Survey on parallel hybrid multigroupcoclustering using collaborative filtering model. International Journal of Advanced Research in Computer and Communication Engineering. 2015; 4(12):536–8.
- Ning X, Karypis G. SLIM: Sparse Linear Methods for Top-N recommender systems. Proceedings of the 2011 IEEE 11thInternational Conference on Data Mining; 2011. p. 497– 506. Crossref. PMid:21364493
- Wu Y, Liu X, Xie M, Ester M, Yang Q. CCCF: Improving collaborative filtering via scalable user-item co-clustering. WSDM 16 San Francisco CA USA Ninth ACMInternational Conference onWeb Search and Data Mining; 2016. p. 73–82.
- Pozo M, Chiky R, Meziane F, Metais E. An item/user representation for Recommender systems based on Bloom filters.Research Challenges in Information Science (RCIS) IEEE Tenth International Conference. 2016; 8:978–90.
- Hackel R, Vlachos M. Scalable and interpretable product recommendations via overlapping co-clustering; 2016.
- Yang Z, Wu B, Zheng K, Wang X, Lei L. A survey of collaborative filtering based recommender systems for mobile internet applications. IEEE Translations and Content Mining. 2016; 21:389–97.
- Rao MVVRMK. A Collaborative Filtering Recommender System with Randomized Learning Rate and Regularized Parameter. Conference: 2016 IEEE International Conference on Current Trends in Advanced; 2016. p.1–5.
- Xiaoyao Z, long LY, Liping S, Fulong C. A new recommender system using context clustering based on matrix factorization techniques. Chinese Journal of Electronics.2016; 25(2):334–40. Crossref.
- Huang SS, Ma J, Wang S. A hybrid multi group co clustering recommendation framework based on information fusion. ACM Transactions on Intelligent Systems and Technology; 2015. p. 1–22. Crossref.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.