LLANIME: Large Language Models for Anime Recommendations

Anjali Agarwal, Sahil Sharma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.
Original languageEnglish
Title of host publication2023 16th International Conference on Developments in eSystems Engineering (DeSE)
PublisherIEEE
ISBN (Electronic)979-8-3503-8134-4
ISBN (Print)979-8-3503-8135-1
DOIs
Publication statusPublished (in print/issue) - 18 Dec 2023
Event2023 16th International Conference on Developments in eSystems Engineering (DeSE) - Instanbul, Turkey
Duration: 18 Dec 202320 Dec 2023

Publication series

Name2023 16th International Conference on Developments in eSystems Engineering (DeSE)
PublisherIEEE Control Society

Conference

Conference2023 16th International Conference on Developments in eSystems Engineering (DeSE)
Abbreviated titleDeSE
Country/TerritoryTurkey
CityInstanbul
Period18/12/2320/12/23

Keywords

  • Collaborative filtering
  • Natural language processing
  • Recommender systems

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