LightGCNxGPT: integrating LightGCN with GPT for enhanced personalised recommendations and explainability in recommender systems
Recommendation systems have emerged as a pivotal tool in shaping our daily choices. Traditional systems face many challenges in providing users with accurate recommendations, especially when there is limited data. Furthermore, these systems fail to provide users with explanations for the recommendat...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175242 |
| _version_ | 1811688552830861312 |
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| author | Tiyyagura, Rochana |
| author2 | Liu Siyuan |
| author_facet | Liu Siyuan Tiyyagura, Rochana |
| author_sort | Tiyyagura, Rochana |
| collection | NTU |
| description | Recommendation systems have emerged as a pivotal tool in shaping our daily choices. Traditional systems face many challenges in providing users with accurate recommendations, especially when there is limited data. Furthermore, these systems fail to provide users with explanations for the recommendations made which is crucial in cultivating trust and transparency. In light of the recent focus on Large Language Models (LLMs), this work proposes a novel framework called LightGCNxGPT that improves recommender systems by employing effective methods such as neighbourhood aggregation and user and item refinement. The LLM based paradigm proposed leverages upon the power of GPT, a popular LLM, to enhance the recommendations made by the state-of-the-art LightGCN model through innovative techniques, namely (i) User Information Refinement (ii) Item Noise Filtering (iii) GPT-Based Explanation Generation. Furthermore, theoretical analysis is provided to support the rationale behind the work and chosen methodology. The experimental results evaluated on a benchmark dataset showcases that the LightGCNxGPT model demonstrates superior performance over current state-of-the-art models. |
| first_indexed | 2024-10-01T05:34:02Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175242 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:34:02Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1752422024-04-26T15:41:59Z LightGCNxGPT: integrating LightGCN with GPT for enhanced personalised recommendations and explainability in recommender systems Tiyyagura, Rochana Liu Siyuan School of Computer Science and Engineering SYLiu@ntu.edu.sg Computer and Information Science Large language models LightGCN GPT LLM Recommendation systems Graph convolutional network Recommendation systems have emerged as a pivotal tool in shaping our daily choices. Traditional systems face many challenges in providing users with accurate recommendations, especially when there is limited data. Furthermore, these systems fail to provide users with explanations for the recommendations made which is crucial in cultivating trust and transparency. In light of the recent focus on Large Language Models (LLMs), this work proposes a novel framework called LightGCNxGPT that improves recommender systems by employing effective methods such as neighbourhood aggregation and user and item refinement. The LLM based paradigm proposed leverages upon the power of GPT, a popular LLM, to enhance the recommendations made by the state-of-the-art LightGCN model through innovative techniques, namely (i) User Information Refinement (ii) Item Noise Filtering (iii) GPT-Based Explanation Generation. Furthermore, theoretical analysis is provided to support the rationale behind the work and chosen methodology. The experimental results evaluated on a benchmark dataset showcases that the LightGCNxGPT model demonstrates superior performance over current state-of-the-art models. Bachelor's degree 2024-04-23T00:58:53Z 2024-04-23T00:58:53Z 2024 Final Year Project (FYP) Tiyyagura, R. (2024). LightGCNxGPT: integrating LightGCN with GPT for enhanced personalised recommendations and explainability in recommender systems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175242 https://hdl.handle.net/10356/175242 en application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Large language models LightGCN GPT LLM Recommendation systems Graph convolutional network Tiyyagura, Rochana LightGCNxGPT: integrating LightGCN with GPT for enhanced personalised recommendations and explainability in recommender systems |
| title | LightGCNxGPT: integrating LightGCN with GPT for enhanced personalised recommendations and explainability in recommender systems |
| title_full | LightGCNxGPT: integrating LightGCN with GPT for enhanced personalised recommendations and explainability in recommender systems |
| title_fullStr | LightGCNxGPT: integrating LightGCN with GPT for enhanced personalised recommendations and explainability in recommender systems |
| title_full_unstemmed | LightGCNxGPT: integrating LightGCN with GPT for enhanced personalised recommendations and explainability in recommender systems |
| title_short | LightGCNxGPT: integrating LightGCN with GPT for enhanced personalised recommendations and explainability in recommender systems |
| title_sort | lightgcnxgpt integrating lightgcn with gpt for enhanced personalised recommendations and explainability in recommender systems |
| topic | Computer and Information Science Large language models LightGCN GPT LLM Recommendation systems Graph convolutional network |
| url | https://hdl.handle.net/10356/175242 |
| work_keys_str_mv | AT tiyyagurarochana lightgcnxgptintegratinglightgcnwithgptforenhancedpersonalisedrecommendationsandexplainabilityinrecommendersystems |