The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문은 추천 시스템과 같은 상황 인식 개인화 애플리케이션을 위한 통계적 선호도 모델을 구축하는 새로운 접근 방식을 제안합니다. 상황인식 통계적 선호모델 구축에 있어서 가장 중요하면서도 어려운 문제 중 하나는 다양한 상황/상황에서 많은 양의 훈련 데이터를 획득하는 것이다. 특히 어떤 상황에서는 이를 설정하거나 그러한 상황에서 문의에 답변할 수 있는 대상을 수집하는 데 많은 작업량이 필요합니다. 이러한 어려움 때문에 실제 상황에서는 단순히 소량의 데이터를 수집하거나, 가정된 상황, 즉 피험자가 특정 상황에 처해 있는 것처럼 가장하는 상황에서 대량의 데이터를 수집하는 경우가 많다. 문의사항에 답변합니다. 그러나 두 접근 방식 모두 문제가 있습니다. 전자의 경우 구축된 선호모델은 데이터의 양이 적어 성능이 좋지 않을 가능성이 높다. 후자의 경우 가정된 상황에서 얻은 데이터가 실제 상황에서 얻은 데이터와 다를 수 있습니다. 그럼에도 불구하고 기존 연구에서는 그 차이를 심각하게 고려하지 않았다. 본 논문에서는 소량의 실제 상황 데이터와 대량의 가정 상황 데이터를 통합하여 더 나은 선호 모델을 얻는 방법을 제안한다. 방법은 음식 선호도에 관한 데이터를 사용하여 평가됩니다. 실험 결과는 선호 모델의 정확도가 크게 향상될 수 있음을 보여줍니다.
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Chihiro ONO, Yasuhiro TAKISHIMA, Yoichi MOTOMURA, Hideki ASOH, Yasuhide SHINAGAWA, Michita IMAI, Yuichiro ANZAI, "Context-Aware Users' Preference Models by Integrating Real and Supposed Situation Data" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 11, pp. 2552-2559, November 2008, doi: 10.1093/ietisy/e91-d.11.2552.
Abstract: This paper proposes a novel approach of constructing statistical preference models for context-aware personalized applications such as recommender systems. In constructing context-aware statistical preference models, one of the most important but difficult problems is acquiring a large amount of training data in various contexts/situations. In particular, some situations require a heavy workload to set them up or to collect subjects capable of answering the inquiries under those situations. Because of this difficulty, it is usually done to simply collect a small amount of data in a real situation, or to collect a large amount of data in a supposed situation, i.e., a situation that the subject pretends that he is in the specific situation to answer inquiries. However, both approaches have problems. As for the former approach, the performance of the constructed preference model is likely to be poor because the amount of data is small. For the latter approach, the data acquired in the supposed situation may differ from that acquired in the real situation. Nevertheless, the difference has not been taken seriously in existing researches. In this paper we propose methods of obtaining a better preference model by integrating a small amount of real situation data with a large amount of supposed situation data. The methods are evaluated using data regarding food preferences. The experimental results show that the precision of the preference model can be improved significantly.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.11.2552/_p
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@ARTICLE{e91-d_11_2552,
author={Chihiro ONO, Yasuhiro TAKISHIMA, Yoichi MOTOMURA, Hideki ASOH, Yasuhide SHINAGAWA, Michita IMAI, Yuichiro ANZAI, },
journal={IEICE TRANSACTIONS on Information},
title={Context-Aware Users' Preference Models by Integrating Real and Supposed Situation Data},
year={2008},
volume={E91-D},
number={11},
pages={2552-2559},
abstract={This paper proposes a novel approach of constructing statistical preference models for context-aware personalized applications such as recommender systems. In constructing context-aware statistical preference models, one of the most important but difficult problems is acquiring a large amount of training data in various contexts/situations. In particular, some situations require a heavy workload to set them up or to collect subjects capable of answering the inquiries under those situations. Because of this difficulty, it is usually done to simply collect a small amount of data in a real situation, or to collect a large amount of data in a supposed situation, i.e., a situation that the subject pretends that he is in the specific situation to answer inquiries. However, both approaches have problems. As for the former approach, the performance of the constructed preference model is likely to be poor because the amount of data is small. For the latter approach, the data acquired in the supposed situation may differ from that acquired in the real situation. Nevertheless, the difference has not been taken seriously in existing researches. In this paper we propose methods of obtaining a better preference model by integrating a small amount of real situation data with a large amount of supposed situation data. The methods are evaluated using data regarding food preferences. The experimental results show that the precision of the preference model can be improved significantly.},
keywords={},
doi={10.1093/ietisy/e91-d.11.2552},
ISSN={1745-1361},
month={November},}
부
TY - JOUR
TI - Context-Aware Users' Preference Models by Integrating Real and Supposed Situation Data
T2 - IEICE TRANSACTIONS on Information
SP - 2552
EP - 2559
AU - Chihiro ONO
AU - Yasuhiro TAKISHIMA
AU - Yoichi MOTOMURA
AU - Hideki ASOH
AU - Yasuhide SHINAGAWA
AU - Michita IMAI
AU - Yuichiro ANZAI
PY - 2008
DO - 10.1093/ietisy/e91-d.11.2552
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2008
AB - This paper proposes a novel approach of constructing statistical preference models for context-aware personalized applications such as recommender systems. In constructing context-aware statistical preference models, one of the most important but difficult problems is acquiring a large amount of training data in various contexts/situations. In particular, some situations require a heavy workload to set them up or to collect subjects capable of answering the inquiries under those situations. Because of this difficulty, it is usually done to simply collect a small amount of data in a real situation, or to collect a large amount of data in a supposed situation, i.e., a situation that the subject pretends that he is in the specific situation to answer inquiries. However, both approaches have problems. As for the former approach, the performance of the constructed preference model is likely to be poor because the amount of data is small. For the latter approach, the data acquired in the supposed situation may differ from that acquired in the real situation. Nevertheless, the difference has not been taken seriously in existing researches. In this paper we propose methods of obtaining a better preference model by integrating a small amount of real situation data with a large amount of supposed situation data. The methods are evaluated using data regarding food preferences. The experimental results show that the precision of the preference model can be improved significantly.
ER -