| Presentation | 2020-05-29 An Efficient Recommendation System Based on Spectral Analysis of Review Data Koki Tozuka, Goutam Chakraborty, Masafumi Matsuhara, Hiroshi Mabuchi, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The purpose of this research is to improve the accuracy of recommendation systems for real-world review data. With increasing popularity of e-commerce, the scale of review data in the real world is enormous, with thousands of items and millions of users. As the review data matrix is extremely sparse, smoothing it to have a sufficiently accurate recommendation system is difficult, using conventional methods of clustering as a model of collaborative filtering. An efficient and accurate tool to have sufficient accuracy is required. In this research, we propose a clustering method for recommendation system that uses matrix spectral clustering, focusing to overcome the problem of large sparseness of review data and find subtle relationship between items. From the experimental results, the proposed method could achieve the highest recommendation accuracy compared to the cluster modelsbased on K-means++, and agglomerative hierarchical clustering. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Recommendation System / Spectral Analysis / Clustering / Laplacian Matrix |
| Paper # | SC2020-2 |
| Date of Issue | 2020-05-22 (SC) |
| Conference Information | |
| Committee | SC |
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| Conference Date | 2020/5/29(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online/Univ of Aizu |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | AI Application for Service Computing Environment and Other Issues |
| Chair | Masahide Nakamura(Kobe Univ.) |
| Vice Chair | Shinji Kikuchi(NIMS) / Yoji Yamato(NTT) |
| Secretary | Shinji Kikuchi(Tokyo Univ. of Tech.) / Yoji Yamato(Fujitsu Lab.) |
| Assistant | |
| Paper Information | |
| Registration To | Technical Committee on Service Computing |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | An Efficient Recommendation System Based on Spectral Analysis of Review Data |
| Sub Title (in English) | |
| Keyword(1) | Recommendation System |
| Keyword(2) | Spectral Analysis |
| Keyword(3) | Clustering |
| Keyword(4) | Laplacian Matrix |
| 1st Author's Name | Koki Tozuka |
| 1st Author's Affiliation | Iwate Prefectural University Graduate School(Iwate Prefectural Univ) |
| 2nd Author's Name | Goutam Chakraborty |
| 2nd Author's Affiliation | Iwate Prefectural University(Iwate Prefectural Univ) |
| 3rd Author's Name | Masafumi Matsuhara |
| 3rd Author's Affiliation | Iwate Prefectural University(Iwate Prefectural Univ) |
| 4th Author's Name | Hiroshi Mabuchi |
| 4th Author's Affiliation | Iwate Prefectural University(Iwate Prefectural Univ) |
| Date | 2020-05-29 |
| Paper # | SC2020-2 |
| Volume (vol) | vol.120 |
| Number (no) | SC-49 |
| Page | pp.pp.7-11(SC), |
| #Pages | 5 |
| Date of Issue | 2020-05-22 (SC) |