LIGER, a new hybrid retrieval model, has been introduced to enhance recommendation systems by combining dense and generative retrieval techniques. This innovative approach aims to address the limitations of both methods while balancing computational efficiency and accuracy, particularly in dealing with cold-start items. LIGER utilizes both item representations and text-based attributes to improve its performance.
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