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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NS | 2024-04-11 15:30 |
Okayama | Okayama Prefectural Library + Online (Primary: On-site, Secondary: Online) |
[Invited Talk]
Trust in the Recommender System and its Prediction based on the Performance Beliefs Yoshinori Hijikata (KGU)  NS2024-4 |
This paper introduces our study on users’ perceived trust in the recommendation system. In recent years, as represented ... [more] | NS2024-4 pp.17-22 |
| LOIS, ICM | 2024-01-25 15:40 |
Nagasaki | Nagasaki Prefectural Art Museum (Primary: On-site, Secondary: Online) |
A Study of Complementary Recommendation Focused on Functional Aspects Kai Sugahara, Chihiro Yamasaki, Yuma Nagi, Kazushi Okamoto (UEC) ICM2023-31 LOIS2023-35 |
Complementary recommendation is a task of recommending items that should be purchased together with an item. In previous... [more] | ICM2023-31 LOIS2023-35 pp.17-22 |
| CQ, CBE (Joint) |
2024-01-25 16:10 |
Kumamoto | Kurokawa-Onsen (Primary: On-site, Secondary: Online) |
[Invited Talk]
Psychological Evaluation of Trust in the Recommender System Yoshinori Hijikata (KGU) CQ2023-58 |
This paper describes the author's development and validation of a recommendation acceptance tendency scale that measures... [more] | CQ2023-58 pp.37-42 |
| HCS, CNR | 2023-11-05 11:15 |
Tokyo | Kogakuin University (Primary: On-site, Secondary: Online) |
Proposal and Evaluation of Recommendation Acceptance Tendency Scale for Overtrust Awareness Yoshinori Hijikata, Reika Miwa, Aika Tsuchida (KGU), Masahiro Hamasaki, Masataka Goto (AIST) CNR2023-10 HCS2023-72 |
As services providing recommendations or judgments of AI (artificial intelligence) become popular, the problem of "recom... [more] | CNR2023-10 HCS2023-72 pp.15-20 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2023-06-29 16:25 |
Okinawa | OIST Conference Center (Primary: On-site, Secondary: Online) |
Minorization-Maximization for Determinantal Point Processes Takahiro Kawashima (SOKENDAI), Hideitsu Hino (ISM/RIKEN) NC2023-7 IBISML2023-7 |
A determinantal point process (DPP) is a powerful probabilistic model that generates diverse random subsets from a groun... [more] | NC2023-7 IBISML2023-7 pp.39-47 |
| DE | 2023-06-16 11:15 |
Tokyo | Musashino University (Primary: On-site, Secondary: Online) |
[Short Paper]
Enhancing User-Controllability in Social Recommender Systems Baofeng Ren, Shin'ichi Konomi (Kyushu Univ.) DE2023-8 |
Social recommender systems have been proposed to improve the effectiveness of various recommendation services, which hav... [more] | DE2023-8 pp.37-40 |
| HCS | 2022-08-27 14:45 |
Hyogo | (Primary: On-site, Secondary: Online) |
Factor Analysis of the Overtrust Scale for Recommender Systems Reika Miwa, Aika Tsuchida, Yoshinori Hijikata (KGU), Masahiro Hamasaki, Masataka Goto (AIST) HCS2022-46 |
Recommender systems have been used in many online services. People may have come to accept the recommended items without... [more] | HCS2022-46 pp.55-60 |
| NS, SR, RCS, SeMI, RCC (Joint) |
2022-07-14 13:35 |
Ishikawa | The Kanazawa Theatre + Online (Primary: On-site, Secondary: Online) |
Building a Federated Personalized Recommendation Model to Balance Similarity and Diversity Masahiro Hamada, Taisho Sasada, Yuzo Taenaka, Youki Kadobayashi (NAIST) NS2022-46 |
With the spread of on-demand movie distribution, personalized movie recommendations that match user preferences are requ... [more] | NS2022-46 pp.100-105 |
| HCS | 2022-01-28 09:40 |
Online | Online | Proposal of an Overtrust Scale of Users in Recommender Systems Aika Tsuchida, Reika Miwa, Yoshinori Hijikata (KGU), Masahiro Hamasaki, Masataka Goto (AIST) HCS2021-43 |
Recommender systems have been used in many online services, and people have been exposed to such recommendations frequen... [more] | HCS2021-43 pp.1-6 |
| IBISML | 2021-03-03 14:25 |
Online | Online | Markov Decision Processes for Simultaneous Control of Multiple Objects with Different State Transition Probabilities in Each Cluster Yuto Motomura, Akira Kamatsuka, Koki Kazama, Toshiyasu Matsushima (Waseda Univ.) IBISML2020-49 |
In this study, we propose an extended MDP model, which is a Markov decision process model with multiple control objects ... [more] | IBISML2020-49 pp.47-54 |
| DE, IPSJ-DBS | 2020-12-22 13:05 |
Online | Online | DE2020-25 |
Web clip and news feed applications have the functions to display a list of contents and save them. If the content is o... [more] |
DE2020-25 pp.48-52 |
| DE | 2020-06-27 10:25 |
Online | Online | Bookmarking Forecast for Others' SNS Posts by Machine Learning of Activity Logs Komei Arasawa, Shun Hattori, Yasuo Kudo (Muroran Inst. of Tech.) DE2020-5 |
An advertising strategy called as "Viral Marketing" has public attention.It promotes an item and its company by using po... [more] | DE2020-5 pp.25-30 |
| DE, IPSJ-DBS | 2019-12-24 09:35 |
Tokyo | National Institute of Informatics | A Book Prediction Model Based on User's Book Arrangement and It's Evaluation Tatsuya Miyamoto, Daisuke Kitayama (Kogakuin Univ.) DE2019-21 |
In recent years, the evaluation of recommender systems has focused on not only accuracy but other aspects. This is bec... [more] |
DE2019-21 pp.1-5 |
| ISEC, SITE, LOIS | 2019-11-02 15:50 |
Osaka | Osaka Univ. | A Note on Encryption-based Recommender Systems Seiya Jumonji, Kazuya Sakai (TMU) ISEC2019-85 SITE2019-79 LOIS2019-44 |
Collaborative filtering recommends unknown contents to a user based on the past behavior or review of the user and is us... [more] | ISEC2019-85 SITE2019-79 LOIS2019-44 pp.149-152 |
| IMQ, IE, MVE, CQ (Joint) [detail] |
2019-03-14 10:50 |
Kagoshima | Kagoshima University | Recommendation for Rental House based on Personal Preference Yang Cao (UEC), Shinichi Nunoya, Yusuke Suzuki, Masachika Suzuki, Yosio Asada (AVANT Corporation), Hiroki Takahashi (UEC) IMQ2018-32 IE2018-116 MVE2018-63 |
For real estate agent, it’s hard to understand users’ preference correctly by vocabulary and make proper recommenda-tion... [more] | IMQ2018-32 IE2018-116 MVE2018-63 pp.55-60 |
| MSS, SS | 2019-01-16 14:05 |
Okinawa | User Preference Extraction Method and Its Rating Scale with Associative Mining and Workflow Net Mohd Anuaruddin Bin Ahmadon (Yamaguchi Univ.), Piyatida Sakorn (Kasetsart Univ.), Shingo Yamaguchi (Yamaguchi Univ.) MSS2018-75 SS2018-46 |
ecommender systems have been widely used to improved customer experienced and to support personalized service to the con... [more] | MSS2018-75 SS2018-46 pp.115-119 |
|
| WIT | 2017-08-29 11:00 |
Akita | Faculty of Engineering Science, Akita Univ. | Recommendation of travel destination through Nonverbal Information using Color Change Prediction Characteristics Extraction Masayoshi Namasu, Sawako Nakajima, Kazutaka Mitobe (Akita Univ.) WIT2017-24 |
The current traveling destination recommendation search system is primarily based on verbal information using words as c... [more] | WIT2017-24 pp.55-59 |
| SC | 2017-06-02 14:20 |
Fukushima | University of Aizu(UBIC 3D) | A Neural Network Recommendation Approach for Improving Accuracy of Multi-criteria Collaborative Filtering Mohammed Hassan, Mohamed Hamada (Univ. of Aizu) SC2017-4 |
Recommender systems (RSs) are intelligent decision-making tools that exploit users? preferences and suggest items that m... [more] | SC2017-4 pp.17-20 |
| SC | 2016-08-26 11:00 |
Tokyo | Kikai-Shinko-Kaikan Bldg. B2F Room No.2 | Improvement of Trust Value Prediction Using Text Mining for Recommender System Incheon Paik, Tomoya Maemori (UoA) SC2016-12 |
Recommender system will help to provide a list of items information that users are interested in. And with growth of soc... [more] | SC2016-12 pp.7-11 |
| RCS, RCC, ASN, NS, SR (Joint) |
2016-07-22 11:40 |
Aichi | Friend Suggestions Method Based on Node Degree Distribution in Social Recommender System Jin-cheng Zhang (USST), Yasuhiro Urayama, Takuji Tachibana (Univ. of Fukui) NS2016-71 |
In some online services such as Amazon, a social recommender system is considered to improve the effective of the recomm... [more] | NS2016-71 pp.109-112 |
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