Language-Model Prior Overcomes Cold-Start Items
This addresses the challenge of recommending new items without interaction data, which is critical for domains like e-commerce and video streaming, though it is an incremental improvement over existing methods.
The paper tackles the cold-start problem in recommender systems by using a language model to estimate item similarities as a Bayesian prior, which boosts performance across various recommenders, as demonstrated on two real-world datasets.
The growth of recommender systems (RecSys) is driven by digitization and the need for personalized content in areas such as e-commerce and video streaming. The content in these systems often changes rapidly and therefore they constantly face the ongoing cold-start problem, where new items lack interaction data and are hard to value. Existing solutions for the cold-start problem, such as content-based recommenders and hybrid methods, leverage item metadata to determine item similarities. The main challenge with these methods is their reliance on structured and informative metadata to capture detailed item similarities, which may not always be available. This paper introduces a novel approach for cold-start item recommendation that utilizes the language model (LM) to estimate item similarities, which are further integrated as a Bayesian prior with classic recommender systems. This approach is generic and able to boost the performance of various recommenders. Specifically, our experiments integrate it with both sequential and collaborative filtering-based recommender and evaluate it on two real-world datasets, demonstrating the enhanced performance of the proposed approach.