MARS:
A
Multimodal Associative Recommendation System
▒ Overall Concept ▒
Recommendation
underlies many internet and web
services. We develop novel recommendation techniques that simulate human
cognitive memory, i.e. crossmodal associative recall between vision and
language. We use machine learning techniques to convert between image and text
using a corpus of articles containing images. Combined with user lifelog and social data, this technology provides personalized crossmodal recommendation services in a mobile environment using smartphones and tablets. This work is supported by the IT R&D Program
of Ministry of Knowledge
Economy under KEIT.
▒
R&D
Objectives (year by year) ▒
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Year
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Subtitle
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Objectives
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2009
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Research on
Information Extraction
in Multimodal Richmedia
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- Attribute definition and relation summarization
of complex information of image, movie and text data
- Framework development for compounding
descriptors
- Mutual generation using image-text cross
modality information based on machine learning
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2010
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Research on Context-based
Information Extraction in Richmedia
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- Methods for context-based extraction
of compound information and descriptor generation
- Cross-modal context analysis in images and
movies
- Multimodal topic modeling with compound
information extracted from richmedia
- Testing service of online article-mall
connection system
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2011
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Research on
Information Extraction of Richmedia in Dynamic Environments
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- Learning and modeling compound information in
time-varying and space-varying data
- Interactive analysis of compound information
in richmedia in incremental way
- Incremental social analysis by multimodal
topic models and its application to microblog analysis
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2012
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Research on
Recommendation Methods based on Multimodal Associativity
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- Multimodal-associative modeling of user
preferences in richmedia
- Interactive recommendation methods in dynamic
richmedia environment
- Multimodal interactive article-mall
connection system with user preference catch and context recognition
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2013
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Development of
MARS and Its Application to Adaptive Recommendation
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- Recommendation engine based on multimodal
association and user preference modeling
- Constructing the framework system of
multimodal associative recommendation system (MARS)
- Personalized/adaptive richmedia
recommendation system
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▒ Target system to be developed: MARS ▒
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▒ Publications
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- Layered
hypernetwork models for cross-modal associative text and image keyword
generation in multimodal information retrieval, J.-W. Ha, B.-H. Kim,
B. Lee, and B.-T. Zhang, Proceedings of the Eleventh Pacific Rim
International Conference on AI (PRICAI2010), 2010.
- Visual
query expansion via incremental hypernetwork models of image and text,
M.-O. Heo, M.-G. Kang, and B.-T. Zhang,
Proceedings of the Eleventh Pacific Rim International Conference on AI
(PRICAI2010), 2010.
- Text-to-image
cross-modal retrieval of magazine articles based on higher-order
pattern recall by hypernetworks, J.-W. Ha, B.-H. Kim, H.-W. Kim, W. C.
Yoon, J.-H. Eom, and B.-T. Zhang, The 10th International Symposium on Advanced
Intelligent Systems (ISIS 2009), pp. 274-277, 2009. (Best paper award)
- Evolutionary
hypernetworks for learning to generate music from examples, H.-W. Kim,
B.-H. Kim, and B.-T. Zhang IEEE International
Conference on Fuzzy Systems (FUZZ-IEEE 2009), pp. 47-52, 2009.
- Gender
classification with cortical thickness measurement, J.-W. Ha, J.H.
Jang, D.-H. Kang, W.H. Jung, J.S. Kwon, and B.-T. Zhang, IEEE
International Conference on Fuzzy Systems (FUZZ-IEEE 2009), pp. 41-46,
2009.
- Learning
word sense disambiguation in biomedical text with difference between
training and test distributions, J.-W. Son and S.-B. Park, In Proc. of
the ACM Third International Workshop on Data and Text Mining in
Bioinformatics, 2009.
- A
weighting scheme for tag recommendation in social bookmarking systems,
S. Ju and K.-B. Hwang, In Proceedings of ECML/PKDD Discovery Challenge
2009. (To appear)
- Evolutionary
hypernetwork classifiers for protein-protein interaction sentence
filtering, J. Bootkrajang, S. Kim, and B.-T. Zhang, The
Genetic and Evolutionary Computation Conference (GECCO 2009), pp.
185-191, 2009.
- An
empirical study of choosing efficient discriminative seeds for
oligonucleotide design, W.-H. Chung and S.-B. Park, In Proc. of Eighth
the International Conference on Bioinformatics, 2009.
- Auto-tagging
method for unlabeled item images with hypernetworks for
article-related item recommender systems, J.-W. Ha, B.-H. Kim, B. Lee,
and B.-T. Zhang, Journal of the Korean Institute of Information
Scientists and Engineers: Computing Practices and Letters, 16(10):1010-1014,
2010.
- Bi-Source 토픽 모델 기법을 이용한 기사-상품 연관 검색, 김병희, 이바도, 하성종, 조남익, 장병탁, 한국정보과학회 가을학술발표 논문집, 제37권 2(A), pp.
74-75, 2010.11.
- 잡지기사 관련 상품 연계 추천 서비스를 위한 하이퍼네트워크 기반의 상품이미지 자동 태깅 기법,하정우,김병희,이바도,장병탁, 2010 한국컴퓨터종합학술대회(KCC2010) 논문집, 제37권 1(A), pp. 123-124,
2010.06.
- 랜덤 하이퍼그래프 모델을 이용한 순차적 멀티모달 데이터에서의 문장 생성, 윤웅창,장병탁, 2010 한국컴퓨터종합학술대회(KCC2010) 논문집, 제37권 1(C), pp.
376-379, 2010.06.
- 진화 하이퍼네트워크를 이용한 음악 학습 및 크로스오버
음악 생성, 김현우, 김병희, 장병탁, 2009 한국컴퓨터종합학술대회논문집,
제36권 1(A), pp.
134-138, 2009.07.
- 하이퍼망 메모리 기반 유아 언어학습 및 생성
모델,
이지훈,
이은석,
장병탁,
2009 한국컴퓨터종합학술대회논문집,
제36권 1(A), pp.
128-129, 2009.07.
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Contact
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Byoung-Hee Kim
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E-Mail
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bhkim -at- bi snu ac kr
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Phone
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+82-2-880-1847
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Fax
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+82-2-875-2240
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