Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design
This addresses the item cold-start issue for recommender systems when content data is unavailable, offering a practical solution with incremental improvements.
The paper tackles the item cold-start problem in collaborative filtering recommenders by formalizing it as an optimization problem to select users for rating new items under a budget constraint, achieving improved prediction error with efficient algorithms validated on the Netflix dataset.
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.