| Project/Area Number |
16K00365
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Kansei informatics
|
|---|
| Research Institution | Muroran Institute of Technology |
|---|
Principal Investigator |
KUDO Yasuo 室蘭工業大学, 大学院工学研究科, 教授 (90360966)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
村井 哲也 千歳科学技術大学, 理工学部, 教授 (90201805)
|
|---|
| Project Period (FY) |
2016-04-01 – 2019-03-31
|
| Project Status |
Completed (Fiscal Year 2018)
|
| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
|
|---|
| Keywords | 情報推薦 / 関係性マイニング / ラフ集合 / 感性情報学 |
|---|
| Outline of Final Research Achievements |
In this study, we proposed a new approach of recommender systems that arrows inconsistency of user's preferences among items. This approach aims at using user’s inconsistent preferences as a kind of user's characteristics of Kansei evaluation to items. To achieve the purpose of this study, first, we used rough set-based interrelationship mining, proposed by us, to directly represent use's preferences as a set of evaluation results between two items, called preference patterns. Preference patterns are used to measure the similarity between the active user and other users and most similar users are selected for recommendation to the active user. Next, we improved the above-mentioned method to treat inconsistency of user's preferences. Moreover, we implemented a recommender system that can treat any preference patters that may include inconsistency of preferences. We conclude that this study can provide a new approach of recommender systems that arrows user's inconsistent preferences.
|
| Academic Significance and Societal Importance of the Research Achievements |
従来の情報推薦技術では，商品群に対するユーザの感性的評価結果は何らかの選好順序で表現されることが多いため，ユーザが商品間で矛盾した選好を暗黙的に有していても，これを直接的に情報推薦に反映させることは困難であった．これに対し本研究では，ユーザの嗜好を関係性属性の集合である選好パタンとして表現することにより，ユーザの嗜好が矛盾を含む場合でも，ユーザによる感性的評価結果として明示的に扱うことができる．これにより，ユーザによる感性的評価結果をより忠実に情報推薦に反映させることが可能となり，本研究はユーザの感性的評価に基づく情報推薦技術の発展に寄与できると考えられる．
|
