| Presentation | 2022-07-14 Building a Federated Personalized Recommendation Model to Balance Similarity and Diversity Masahiro Hamada, Taisho Sasada, Yuzo Taenaka, Youki Kadobayashi, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | With the spread of on-demand movie distribution, personalized movie recommendations that match user preferences are required to improve service quality and retention rates. In recent years, it has become clear that the retention rate can be improved by recommending not only movies similar to the user’s favorite movies. Diversity is also used as an important indicator ．In general, movie recommendation uses viewing history and evaluation scores to select recommended movies, and video distributors must store and use user information. However, based on EU General Data Protection Regulation (GDPR), there are restrictions on the retention and use of such data that can be used to infer personal tastes and thoughts, and this creates a problem that individualized movie recommendations cannot be made based on the user’s viewing log. In contrast, the use of Federative Learning (FL), which can recommend movies without holding data, has been attracting attention, but since training data is trained on the user’s terminal, it tends to learn too much about the tendencies of each terminal. Therefore, we propose a method for constructing a privacy protection recommendation model that achieves both similarity and diversity. By selecting training data in a way that does not impair either similarity or diversity, and building a mechanism for learning on each terminal, we aim to construct a privacy-protective recommendation model that recommends a variety of movies while maintaining similarity to the browsing log. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Federated Leanring / Bayesian Personalized Ranking / Matrix Factorization / Recommender System |
| Paper # | NS2022-46 |
| Date of Issue | 2022-07-06 (NS) |
| Conference Information | |
| Committee | NS / SR / RCS / SeMI / RCC |
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| Conference Date | 2022/7/13(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | The Kanazawa Theatre + Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Distributed Wireless Network, M2M (Machine-to-Machine)，D2D (Device-to-Device)，IoT（Internet of Things）, etc |
| Chair | Tetsuya Oishi(NTT) / Suguru Kameda(Hiroshima Univ.) / Kenichi Higuchi(Tokyo Univ. of Science) / Koji Yamamoto(Kyoto Univ.) / Shunichi Azuma(Nagoya Univ.) |
| Vice Chair | Takumi Miyoshi(Shibaura Insti of Tech.) / Osamu Takyu(Shinshu Univ.) / Kentaro Ishidu(NICT) / Kazuto Yano(ATR) / Tomoya Tandai(Toshiba) / Fumihide Kojima(NICT) / Osamu Muta(Kyushu Univ.) / Kazuya Monden(Hitachi) / Yasunori Owada(NICT) / Shunsuke Saruwatari(Osaka Univ.) / Shunichi Azuma(Hokkaido Univ.) / Koji Ishii(Kagawa Univ.) |
| Secretary | Takumi Miyoshi(NTT) / Osamu Takyu(Kogakuin Univ.) / Kentaro Ishidu(Mie Univ.) / Kazuto Yano(Tokai Univ.) / Tomoya Tandai(NTT) / Fumihide Kojima(Panasonic) / Osamu Muta(Univ. of Electro-Comm) / Kazuya Monden(Sharp) / Yasunori Owada(NTT DOCOMO) / Shunsuke Saruwatari(Tokyo Univ. of Agri. and Tech.) / Shunichi Azuma(Osaka Univ.) / Koji Ishii(CRIEPI) |
| Assistant | Kotaro Mihara(NTT) / Taichi Ohtsuji(NEC) / WANG Xiaoyan(Ibaraki Univ.) / Akemi Tanaka(MathWorks) / Katsuya Suto(Univ. of Electro-Comm) / Manabu Sakai(Mitsubishi Electric) / Masashi Iwabuchi(NTT) / Tatsuki Okuyama(NTT DOCOMO) / Issei Kanno(KDDI Research) / Yuyuan Chang(Tokyo Inst. of Tech) / Yuki Matsuda(NAIST) / Akihito Taya(Aoyama Gakuin Univ.) / Takeshi Hirai(Osaka Univ.) / SHAN LIN(NICT) / Ryosuke Adachi(Yamaguchi Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Network Systems / Technical Committee on Smart Radio / Technical Committee on Radio Communication Systems / Technical Committee on Sensor Network and Mobile Intelligence / Technical Committee on Reliable Communication and Control |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Building a Federated Personalized Recommendation Model to Balance Similarity and Diversity |
| Sub Title (in English) | |
| Keyword(1) | Federated Leanring |
| Keyword(2) | Bayesian Personalized Ranking |
| Keyword(3) | Matrix Factorization |
| Keyword(4) | Recommender System |
| 1st Author's Name | Masahiro Hamada |
| 1st Author's Affiliation | Nara Institute of Science and Technology(NAIST) |
| 2nd Author's Name | Taisho Sasada |
| 2nd Author's Affiliation | Nara Institute of Science and Technology(NAIST) |
| 3rd Author's Name | Yuzo Taenaka |
| 3rd Author's Affiliation | Nara Institute of Science and Technology(NAIST) |
| 4th Author's Name | Youki Kadobayashi |
| 4th Author's Affiliation | Nara Institute of Science and Technology(NAIST) |
| Date | 2022-07-14 |
| Paper # | NS2022-46 |
| Volume (vol) | vol.122 |
| Number (no) | NS-105 |
| Page | pp.pp.100-105(NS), |
| #Pages | 6 |
| Date of Issue | 2022-07-06 (NS) |