ABSTRACT
It is well known that collaborative filtering (CF) based recommender systems provide better modeling of users and items associated with considerable rating history. The lack of historical ratings results in the user and the item cold-start problems. The latter is the main focus of this work. Most of the current literature addresses this problem by integrating content-based recommendation techniques to model the new item. However, in many cases such content is not available, and the question arises is whether this problem can be mitigated using CF techniques only. We formalize this problem as an optimization problem: given a new item, a pool of available users, and a budget constraint, select which users to assign with the task of rating the new item in order to minimize the prediction error of our model. We show that the objective function is monotone-supermodular, and propose efficient optimal design based algorithms that attain an approximation to its optimum. Our findings are verified by an empirical study using the Netflix dataset, where the proposed algorithms outperform several baselines for the problem at hand.
- Deepak Agarwal and Bee-Chung Chen. Regression-based latent factor models. In Proc. KDD, 2009. Google ScholarDigital Library
- Michal Aharon, Amit Kagian, Yehuda Koren, and Ronny Lempel. Dynamic personalized recommendation of comment-eliciting stories. In Proc. RecSys, 2012. Google ScholarDigital Library
- Natalie Aizenberg, Yehuda Koren, and Oren Somekh. Build your own music recommender by modeling internet radio streams. In Proc. WWW, 2012. Google ScholarDigital Library
- A. C. Atkinson and A. N. Donev. Optimum Experimental Designs. Oxford Univ. Press, 1992.Google Scholar
- J. Bennett and S. Lanning. The net ix prize. In Proc. KDD Cup and Workshop, 2007.Google Scholar
- Gideon Dror, Noam Koenigstein, and Yehuda Koren. Yahoo! music recommendations: Modeling music ratings with temporal dynamics and item. In Proc. RecSys, 2011. Google ScholarDigital Library
- John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research (JMLR), 12:2121--2159, 2011. Google ScholarDigital Library
- Nadav Golbandi, Yehuda Koren, and Ronny Lempel. On bootstrapping recommender systems. In Proc. CIKM, 2010. Google ScholarDigital Library
- Nadav Golbandi, Yehuda Koren, and Ronny Lempel. Adaptive bootstrapping of recommender systems using decision trees. In Proc. WSDM, 2011. Google ScholarDigital Library
- Asela Gunawardana and Christopher Meek. Tied boltzmann machines for cold start recommendations. In Proc. RecSys, 2008. Google ScholarDigital Library
- Asela Gunawardana and Christopher Meek. A unified approach to building hybrid recommender systems. In Proc. RecSys, 2009. Google ScholarDigital Library
- Victor P. Il'ev. An approximation guarantee of the greedy descent algorithm for minimizing a supermodular set function. Discrete Applied Mathematics, 114(1):131--146, 2001.Google ScholarCross Ref
- Arnd Kohrs and Bernard Merialdo. Improving collaborative filtering for new users by smart object selection. In Proc. International Conference on Media Features (ICMF), 2001.Google Scholar
- Yehuda Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proc. KDD, 2008. Google ScholarDigital Library
- Yehuda Koren. Collaborative filtering with temporal dynamics. Commun. of the ACM, 53(4):89--97, 2010. Google ScholarDigital Library
- Yehuda Koren, Robert M. Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, IEEE, 42(8):30--37, 2009. Google ScholarDigital Library
- Andreas Krause and Carlos Guestrin. Near-optimal observation selection using submodular functions. In AAAI, volume 7, pages 1650--1654, 2007. Google ScholarDigital Library
- Shao-Lun Lee. Commodity recommendations of retail business based on decision tree induction. Expert Systems with Applications, 37(5):3685--3694, 2010. Google ScholarDigital Library
- Seung-Taek Park and Wei Chu. Pairwise preference regression for cold-start recommendation. In Proc. RecSys, 2009. Google ScholarDigital Library
- Arkadiusz Paterek. Improving regularized singular value decomposition for collaborative filtering. In Proc. KDD cup and workshop, 2007.Google Scholar
- Al Mamunur Rashid, Istvan Albert, Dan Cosley, Shyong K Lam, Sean M McNee, Joseph A Konstan, and John Riedl. Getting to know you: learning new user preferences in recommender systems. In Proc. International Conference on Intelligent User Interfaces, 2002. Google ScholarDigital Library
- Al Mamunur Rashid, George Karypis, and John Riedl. Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explorations Newsletter, 10(2):90--100, 2008. Google ScholarDigital Library
- Guillaume Sagnol. Approximation of a maximum-submodular-coverage problem involving spectral functions, with application to experimental designs. Discrete Applied Mathematics, 161(1):258--276, 2013. Google ScholarDigital Library
- Chien-Fu Wu. Some algorithmic aspects of the theory of optimal designs. Annals of Statistics, 6(6):1286--1301, 1978.Google ScholarCross Ref
- Kai Yu, Jinbo Bi, and Volker Tresp. Active learning via transductive experimental design. In Proc. ICML, 2006. Google ScholarDigital Library
- Ke Zhou, Shuang-Hong Yang, and Hongyuan Zha. Functional matrix factorizations for cold-start recommendation. In Proc. SIGIR, 2011. Google ScholarDigital Library
Index Terms
- Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design
Recommendations
Wasserstein Collaborative Filtering for Item Cold-start Recommendation
UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and PersonalizationItem cold-start recommendation, which predicts user preference on new items that have no user interaction records, is an important problem in recommender systems. In this paper, we model the disparity between user preferences on warm items (those having ...
Merging trust in collaborative filtering to alleviate data sparsity and cold start
Providing high quality recommendations is important for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Collaborative filtering is a widely accepted technique to generate recommendations based on ...
Improving Cold Start Recommendation by Mapping Feature-Based Preferences to Item Comparisons
UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and PersonalizationMany Recommender Systems (RSs) rely on user preference data in the form of ratings or likes for items. Previous research has shown that item comparisons can also be effectively used to model user preferences and build RS. However, users often express ...
Comments