Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design
Oren Anava, Shahar Golan, Nadav Golbandi, Zohar Karnin, Ronny Lempel,, Oleg Rokhlenko, Oren Somekh

TL;DR
This paper addresses the item cold-start problem in collaborative filtering by formulating it as an optimization task to select users for rating within a budget, using supermodular optimization techniques, and demonstrates improved performance on Netflix data.
Contribution
It introduces a novel CF-only approach for item cold-start handling via optimal design, avoiding content-based methods, with algorithms that approximate the optimal user selection.
Findings
Proposed algorithms outperform baselines on Netflix dataset.
Objective function is proven to be monotone-supermodular.
Efficient algorithms achieve near-optimal user selection for cold-start items.
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…
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Taxonomy
TopicsRecommender Systems and Techniques · Advanced Bandit Algorithms Research · Expert finding and Q&A systems
