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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 |
| 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 |
| 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 |
| 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 |
| 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 |
|
| 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 |
| IBISML | 2013-07-18 13:25 |
Tokyo | Nishiwaseda Campus (Waseda univ.) | A Simultaneous Completion Method for Multiple Relational Data Yutaka Ieiri, Hisashi Kashima (Univ. Tokyo) IBISML2013-6 |
In this paper, we consider a completion problem of multiple relational data sets with missing values. In cases where ... [more] |
IBISML2013-6 pp.35-41 |
| IBISML | 2013-07-18 13:50 |
Tokyo | Nishiwaseda Campus (Waseda univ.) | An improvement of a Neutrality Term in an Information-neutral Recommender System Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh (AIST), Jun Sakuma (Univ. of Tsukuba) IBISML2013-7 |
Information-neutral recommender systems aim to make recommendations whose neutrality from the specified viewpoint is gua... [more] | IBISML2013-7 pp.43-50 |
| LOIS | 2012-03-09 15:40 |
Okinawa | Meio Univ. | Relevance Modeling of Linked Open Data and Users' Transaction Histories for Recommendation Robert Sumi, Yutaka Kabutoya, Tomoharu Iwata, Toshio Uchiyama, Ko Fujimura (NTT) LOIS2011-108 |
We propose using Linked Open Data (LOD) to inform a topic model for recommending both well-watched old movies and unwatc... [more] | LOIS2011-108 pp.213-218 |
| AI | 2011-11-21 15:20 |
Fukuoka | Rule Extraction from Twitter Using Inductive Logic Programming Noriaki Chikara (TCT), Miyuki Koshimura, Hiroshi Fujita, Ryuzo Hasegawa (Kyushu Univ.) AI2011-25 |
There are a lot of information recommender systems on the Web. These systems aim to find and provide useful information ... [more] | AI2011-25 pp.49-51 |
|
| IBISML | 2010-11-04 15:00 |
Tokyo | IIS, Univ. of Tokyo | [Poster Presentation]
An Application of Generalized Linear Model for Recommender System
-- Rating Estimation Based on Main-Effect Model -- Yu Fujimoto (Aoyama Gakuin Univ.) IBISML2010-68 |
Collaborative filtering based on a rating matrix is broadly used in recommender systems. In a practical situation, the m... [more] | IBISML2010-68 pp.65-71 |
| IBISML | 2010-06-14 16:55 |
Tokyo | Takeda Hall, Univ. Tokyo | Customized Pricing Recommender System
-- Simple Implementation and Preliminary Experiments -- Toshihiro Kamishima, Shotaro Akaho (AIST), Jun Sakuma (Univ. of Tsukuba/JST) IBISML2010-11 |
Recommender systems suggests items that would be preferred to customers. Here, we propose to add new function, price dis... [more] | IBISML2010-11 pp.63-69 |
| AI | 2010-01-22 11:00 |
Tokyo | Reduction technique of user rating histories for information filtering systems Takayuki Uda (IISEC), Tetsuo Kinoshita (TOHOKU Univ.) AI2009-21 |
The information filtering system refers to action histories and evaluation histories of the user to perform filtering. ... [more] | AI2009-21 pp.13-18 |
|
| AI, IPSJ-ICS, JSAI-KBS | 2009-03-03 16:25 |
Miyagi | Laforet Zao Resort & Spa | Adaptive Fusion of User-based and Item-based Collaborative Filtering
-- Empirical Analysis Using MovieLens Dataset -- Akihiro Yamashita (Hokkaido Univ/JSPS), Hidenori Kawamura, Keiji Suzuki, Azuma Ohuchi (Hokkaido Univ.) |
In many E-commerce sites, recommender systems, which provide personalized recommendation from among a large number of it... [more] | AI2008-82 pp.105-110 |
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