A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning

Normalized discounted cumulative gain (NDCG) is one of the popular evaluation metrics for recommender systems and learning-to-rank problems. As it is non-differentiable, it cannot be optimized by gradient-based optimization procedures. In the last twenty years, a plethora of surrogate losses have been engineered that aim to make learning recommendation and ranking models that optimize NDCG possible. However, binary relevance implicit feedback settings still pose a significant challenge for such surrogate losses as they are usually designed and evaluated only for multi-level relevance feedback. In this paper, we address the limitations of directly optimizing the NDCG measure by proposing a guided learning approach (GuidedRec) that adopts recent advances in parameterized surrogate losses for NDCG. Starting from the observation that jointly learning a surrogate loss for NDCG and the recommendation model is very unstable, we design a stepwise approach that can be seamlessly applied to any recommender system model that uses a point-wise logistic loss function. The proposed approach guides the models towards optimizing the NDCG using an independent surrogate-loss model trained to approximate the true NDCG measure while maintaining the original logistic loss function as a stabilizer for the guiding procedure. In experiments on three recommendation datasets, we show that our guided surrogate learning approach yields models better optimized for NDCG than recent state-of-the-art approaches using engineered surrogate losses.