Experimental comparison of recommender systems
Recommender systems seek to predict the rating that a user would give an item, given the data of the past ratings of all users and items and other side information. Traditionally, recommender system methods are split into two broad categories: content-based and collaborative filtering approaches. Ho...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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2019
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| Online Access: | http://hdl.handle.net/10356/76935 |
| _version_ | 1811696148074725376 |
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| author | See, Jie Xun |
| author2 | Xavier Bresson |
| author_facet | Xavier Bresson See, Jie Xun |
| author_sort | See, Jie Xun |
| collection | NTU |
| description | Recommender systems seek to predict the rating that a user would give an item, given the data of the past ratings of all users and items and other side information. Traditionally, recommender system methods are split into two broad categories: content-based and collaborative filtering approaches. However, because of the graph-structured nature of data in recommender system tasks, graph neural networks hold much promise in pushing the state-of-the-art in recommender systems. This project aims to investigate the latest graph neural network approaches to recommender systems. In particular, it aims to incorporate the use of Residual Gated Graph ConvNets, an architecture that has proven effective on various graph learning tasks, into the recommender system task. We show that within a framework similar to collaborative filtering, using graph neural networks can produce competitive results across various benchmark datasets. |
| first_indexed | 2024-10-01T07:34:45Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/76935 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:34:45Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/769352023-03-03T20:27:44Z Experimental comparison of recommender systems See, Jie Xun Xavier Bresson School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition Recommender systems seek to predict the rating that a user would give an item, given the data of the past ratings of all users and items and other side information. Traditionally, recommender system methods are split into two broad categories: content-based and collaborative filtering approaches. However, because of the graph-structured nature of data in recommender system tasks, graph neural networks hold much promise in pushing the state-of-the-art in recommender systems. This project aims to investigate the latest graph neural network approaches to recommender systems. In particular, it aims to incorporate the use of Residual Gated Graph ConvNets, an architecture that has proven effective on various graph learning tasks, into the recommender system task. We show that within a framework similar to collaborative filtering, using graph neural networks can produce competitive results across various benchmark datasets. Bachelor of Engineering (Computer Science) 2019-04-24T14:08:03Z 2019-04-24T14:08:03Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/76935 en Nanyang Technological University 34 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition See, Jie Xun Experimental comparison of recommender systems |
| title | Experimental comparison of recommender systems |
| title_full | Experimental comparison of recommender systems |
| title_fullStr | Experimental comparison of recommender systems |
| title_full_unstemmed | Experimental comparison of recommender systems |
| title_short | Experimental comparison of recommender systems |
| title_sort | experimental comparison of recommender systems |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition |
| url | http://hdl.handle.net/10356/76935 |
| work_keys_str_mv | AT seejiexun experimentalcomparisonofrecommendersystems |