beeFormer: Bridging the Semantic and Interaction Gap in Recommender Systems

The beeFormer project introduces a novel approach to recommender systems designed to tackle the cold-start problem. It leverages language models to learn user behavior patterns from interaction data and transfer this knowledge to unseen items. Unlike traditional content-based filtering which relies on item attributes, beeFormer learns user interaction patterns to better recommend items aligned with user interests, even with no prior interaction data. Experiments demonstrate significant performance improvements. The project provides detailed training steps and pre-trained models, supporting datasets such as MovieLens, GoodBooks, and Amazon Books.