| Paper Abstract and Keywords |
| Presentation |
2021-02-18 16:15
A Note on Cross-domain Recommendation Based on Multilayer Graph Analysis with Visual Features
Taisei Hirakawa, Keisuke Maeda, Takahiro Ogawa (Hokkaido Univ.), Satoshi Asamizu (NIT, Kushiro College), Miki Haseyama (Hokkaido Univ.) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
In this paper, we improve the accuracy of cross-domain recommendation methods, which have been recently studied as a solution to the cold-start problem, by introducing visual features obtained from product images. The proposed method, first calculates the item features considering the visual features of product images. Next, we construct graphs using the calculated item features and the user embedding obtained from the users' purchase histories and estimate the embedding features based on a graph neural network. Specifically, the proposed method adopts visual features as the initialization of the network, and consideration of visual features becomes feasible. Based on the relationship between the embedding features of each domain, item recommendation can be realized. The effectiveness of the proposed method is confirmed through experiments using a large dataset based on purchase histories. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Cross-domain Recommendation / multi-layer graph analysis / visual features / graph neural network / / / / |
| Reference Info. |
ITE Tech. Rep., vol. 45, no. 4, ME2021-12, pp. 59-63, Feb. 2021. |
| Paper # |
ME2021-12 |
| Date of Issue |
2021-02-11 (MMS, ME, AIT) |
| ISSN |
Print edition: ISSN 1342-6893 Online edition: ISSN 2424-1970 |
| Download PDF |
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| Conference Information |
| Committee |
IEICE-IE IEICE-ITS MMS ME AIT |
| Conference Date |
2021-02-18 - 2021-02-19 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Online |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
Image Processing, etc. |
| Paper Information |
| Registration To |
ME |
| Conference Code |
2021-02-IE-ITS-MMS-ME-AIT |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
A Note on Cross-domain Recommendation Based on Multilayer Graph Analysis with Visual Features |
| Sub Title (in English) |
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| Keyword(1) |
Cross-domain Recommendation |
| Keyword(2) |
multi-layer graph analysis |
| Keyword(3) |
visual features |
| Keyword(4) |
graph neural network |
| Keyword(5) |
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| 1st Author's Name |
Taisei Hirakawa |
| 1st Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
| 2nd Author's Name |
Keisuke Maeda |
| 2nd Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
| 3rd Author's Name |
Takahiro Ogawa |
| 3rd Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
| 4th Author's Name |
Satoshi Asamizu |
| 4th Author's Affiliation |
National Institute of Technology, Kushiro College (NIT, Kushiro College) |
| 5th Author's Name |
Miki Haseyama |
| 5th Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
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| Speaker |
Author-1 |
| Date Time |
2021-02-18 16:15:00 |
| Presentation Time |
25 minutes |
| Registration for |
ME |
| Paper # |
MMS2021-12, ME2021-12, AIT2021-12 |
| Volume (vol) |
vol.45 |
| Number (no) |
no.4 |
| Page |
pp.59-63 |
| #Pages |
5 |
| Date of Issue |
2021-02-11 (MMS, ME, AIT) |
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