LLANIME: Large Language Models for Anime Recommendations

Large Language Models (LLMs) have advanced significantly in Natural Language Processing (NLP) over the past few years. Ongoing research continues exploring their capabilities in recommendation systems, aiming to enhance user-tailored content delivery efficiency, accuracy, and personalisation. The investigation introduces a novel approach to integration possibilities of open-source Language Model (LLM) technology—FLAN-T5, Falcon, Vicuna, UL2, and LLAMA—into anime recommendation systems. The research delves into creating personalised recommendations by inputting anime titles, genres, and descriptions into these LLMs. Furthermore, it harnesses LLMs to explain these recommendations, bolstering user engagement and amplifying transparency in the recommendation process. The findings clearly show that using open-source LLMs for anime recommendations works well. It proves that these techniques have great potential to make anime suggestions better.