ABSTRACT
We study the problem of learning personalized user models from rich user interactions. In particular, we focus on learning from clustering feedback (i.e., grouping recommended items into clusters), which enables users to express similarity or redundancy between different items. We propose and study a new machine learning problem for personalization, which we call collaborative clustering. Analogous to collaborative filtering, in collaborative clustering the goal is to leverage how existing users cluster or group items in order to predict similarity models for other users' clustering tasks. We propose a simple yet effective latent factor model to learn the variability of similarity functions across a user population. We empirically evaluate our approach using data collected from a clustering interface we developed for a goal-oriented data exploration (or sensemaking) task: asking users to explore and organize attractions in Paris. We evaluate using several realistic use cases, and show that our approach learns more effective user models than conventional clustering and metric learning approaches.
- E. Acar, D. M. Dunlavy, T. G. Kolda, and M. Mørup. Scalable tensor factorizations with missing data. In SIAM Conference on Data Mining (SDM), 2010.Google ScholarCross Ref
- S. Amershi, J. Fogarty, and D. Weld. Regroup: Interactive machine learning for on-demand group creation in social networks. In ACM Conference on Human Factors in Computing Systems (CHI), 2012. Google ScholarDigital Library
- M. Balcan and A. Blum. Clustering with interactive feedback. In International Conference on Algorithmic Learning Theory (ALT), 2008. Google ScholarDigital Library
- S. Basu, M. Bilenko, and R. J. Mooney. A probabilistic framework for semi-supervised clustering. In ACM Conference on Knowledge Discovery and Data Mining (KDD), 2004. Google ScholarDigital Library
- S. Basu, D. Fisher, S. Drucker, and H. Lu. Assisting users with clustering tasks by combining metric learning and classification. In National Conference on Artificial Intelligence (AAAI), 2010.Google Scholar
- J. Blitzer and J. Weston. Latent structured ranking. In Conference on Uncertainty in Artificial Intelligence (UAI), 2012.Google Scholar
- S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternatiing direction method of multipliers. Foundations and Trends in Machine Learning, 3(1):1--122, 2011. Google ScholarDigital Library
- C. Brandt, T. Joachims, Y. Yue, and J. Bank. Dynamic ranked retrieval. In ACM Conference on Web Search and Data Mining (WSDM), 2011. Google ScholarDigital Library
- D. H. Chau, A. Kittur, J. I. Hong, and C. Faloutsos. Apolo: Making sense of large network data by combining rich user interaction and machine learning. In ACM Conference on Human Factors in Computing Systems (CHI), 2011. Google ScholarDigital Library
- J. Davis, B. Kulis, P. Jain, S. Sra, and I. Dhillon. Information-theoretic metric learning. In International Conference on Machine Learning (ICML), 2007. Google ScholarDigital Library
- T. Evgeniou and M. Pontil. Regularized multi-task learning. In ACM Conference on Knowledge Discovery and Data Mining (KDD), 2004. Google ScholarDigital Library
- G. Forestier, P. Gançarski, and C. Wemmert. Collaborative clustering with background knowledge. Journal of Data & Knowledge Engineering, 69(2):211--228, 2010. Google ScholarDigital Library
- R. Gomes, P. Welinder, A. Krause, and P. Perona. Crowdclustering. In Neural Information Processing Systems (NIPS), 2011.Google Scholar
- K. Hammouda and M. Kamel. Collaborative document clustering. In SIAM Conference on Data Mining (SDM), 2006.Google ScholarCross Ref
- Y. Koren and R. Bell. Advances in collaborative filtering. In Recommender Systems Handbook, pages 145--186. Springer, 2011.Google ScholarCross Ref
- Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8):30--37, 2009. Google ScholarDigital Library
- L. Li, W. Chu, J. Langford, and R. Schapire. A contextual-bandit approach to personalized news article recommendation. In World Wide Web Conference (WWW), 2010. Google ScholarDigital Library
- N. Nello Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, 2000. Google ScholarDigital Library
- D. Niu, J. Dy, and M. Jordan. Multiple non-redundant spectral clustering views. In International Conference on Machine Learning (ICML), 2010.Google Scholar
- S. Parameswaran and K. Weinberger. Large margin multi-task metric learning. In Neural Information Processing Systems (NIPS), 2010.Google Scholar
- D. M. Russell, M. J. Stefik, P. Pirolli, and S. K. Card. The cost structure of sensemaking. In Proceedings of the INTERACT'93 and CHI'93 conference on Human factors in computing systems, 1993. Google ScholarDigital Library
- R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In Neural Information Processing Systems (NIPS), 2008.Google Scholar
- G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5):513--523, 1988. Google ScholarDigital Library
- M. Schultz and T. Joachims. Learning a distance metric from relative comparisons. In Neural Information Processing Systems (NIPS), 2003.Google ScholarDigital Library
- D. Shahaf, J. Yang, C. Suen, J. Jacobs, H. Wang, and J. Leskovec. Information cartography: Creating zoomable, large-scale maps of information. In ACM Conference on Knowledge Discovery and Data Mining (KDD), 2013. Google ScholarDigital Library
- N. Srebro. Learning with Matrix Factorizations. PhD thesis, Massachusetts Institute of Technology, 2004. Google ScholarDigital Library
- I. Sutskever, R. Salakhutdinov, and J. Tenenbaum. Modelling relational data using Bayesian clustered tensor factorization. In Neural Information Processing Systems (NIPS), 2009.Google Scholar
- O. Tamuz, C. Liu, S. Belongie, O. Shamir, and A. T. Kalai. Adaptively learning the crowd kernel. In International Conference on Machine Learning (ICML), 2011.Google ScholarDigital Library
- K. Wagstaff and C. Cardie. Clustering with instance-level constraints. In National Conference on Artificial Intelligence (AAAI), 2000. Google ScholarDigital Library
- C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In ACM Conference on Knowledge Discovery and Data Mining (KDD), 2011. Google ScholarDigital Library
- E. Xing, A. Ng, M. Jordan, and S. Russell. Distance metric learning, with application to clustering with side-information. In Neural Information Processing Systems (NIPS), 2002.Google Scholar
- Y. Zhang and D. Yeung. Transfer metric learning by learning task relationships. In ACM Conference on Knowledge Discovery and Data Mining (KDD), 2010. Google ScholarDigital Library
Index Terms
- Personalized collaborative clustering
Recommendations
A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems
UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and PersonalizationMuch of the focus of recommender systems research has been on the accurate prediction of users' ratings for unseen items. Recent work has suggested that objectives such as diversity and novelty in recommendations are also important factors in the ...
Adaptive personalized recommender system using learning automata and items clustering
AbstractThe personalized recommender systems provide user-related services based on user preferences; these preferences are recorded in an individual profile. Therefore, the more complete and precise each user profile leads more successful the ...
Highlights- Construct Adaptive user’s interest model using learning automata.
- Present ...
Generalized Collaborative Personalized Ranking for Recommendation
Web and Big DataAbstractData sparsity is a common problem in collaborative ranking for personalized recommendation with implicit feedback. Several previous work tried to ‘borrow’ feedback information from users’ neighborhood as their prior preferences to alleviate this ...
Comments