Combining heterogeneous sources in an interactive multimedia content retrieval model |
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| Affiliation: | 1. Deutches Forschungzentrum für Künstliches Intelligenz - DFKI, Alt-Moabit, 91c, 10559, Berlin, Germany;2. Computer Science Department, Universidad Carlos III de Madrid, Avda. Universidad, 30, 28911, Leganés, Madrid, Spain;3. MeaningCloud LLC, USA;1. Salford Business School, University of Salford, 43 Crescent, Salford M5 4WT, UK;2. Division of Mathematics and Computation, School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Minshull House, 47-49 Chorlton St, Manchester M1 3FY, UK;3. Institute of Management Science and Engineering, Business School, Henan University, 475004, Jinming District, Kaifeng, Henan Province, China;4. Faculty of Software, Fujian Normal University, Upper 3rd Rd, Cangshan, Fuzhou, Fujian Province, 350108, China;5. School of Computing, Science & Engineering, University of Salford, 43 Crescent, Salford M5 4WT, UK;1. Department of Economics and Statistics, University of Naples Federico II, Italy;2. Department of Industrial Engineering, University of Naples Federico II, Italy;1. Department of Nuclear Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea;2. Central Research Institute, Korea Hydro & Nuclear Power Co. LTD, Yuseong-gu, Daejeon 34101, South Korea;1. Faculty of Information Technology, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam;2. College of Electronics and Information Engineering, Sejong University, Seoul, Republic of Korea;3. Division of Data Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam;4. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam;5. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6R 2V4 AB, Canada;6. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia;7. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;1. Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain;2. Centre for Secure Information Technologies, School of EEECS, Queens University Belfast, BT3 9DT, UK |
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| Abstract: | Interactive multimodal information retrieval systems (IMIR) increase the capabilities of traditional search systems, by adding the ability to retrieve information of different types (modes) and from different sources. This article describes a formal model for interactive multimodal information retrieval. This model includes formal and widespread definitions of each component of an IMIR system. A use case that focuses on information retrieval regarding sports validates the model, by developing a prototype that implements a subset of the features of the model. Adaptive techniques applied to the retrieval functionality of IMIR systems have been defined by analysing past interactions using decision trees, neural networks, and clustering techniques. This model includes a strategy for selecting sources and combining the results obtained from every source. After modifying the strategy of the prototype for selecting sources, the system is re-evaluated using classification techniques. This evaluation compares the normalised discounted cumulative gain (NDCG) measure obtained using two different approaches: the multimodal system using a baseline strategy based on predefined rules as a source selection strategy, and the same multimodal system with the functionality adapted by past user interactions. In the adapted system, a final value of 81,54% was obtained for the NDCG. |
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