The Semantic Web aims at building a Web where data is enriched with meaningful annotations. In other words, data is semantically organized in such a way that both human and machine can understand and query it, aiming at the creation of dynamic Web pages. Ontologies, as a keystone of the Semantic Web, have gained an ample acceptance as an information model, which can be used for several purposes, such as information retrieval in the Web. However, data is normally stored in databases, which present various problems in the Semantic Web context, because data is not semantically annotated. Aiming at retrieving rich results in the sense of meaning, several ways of relating databases with ontologies have emerged. This paper presents a mapping – with the aid of a framework called Ontop – as a solution for the communication problem between the relational database of the Emigration Museum of Fafe (EMF) and the ontology of the Emigration Museum (OntoME), which describes the Cultural Heritage domain. This mapping will be used to realize the CaVa architecture, aiming at the creation of dynamic Web pages as virtual Learning Spaces. Real examples of the mapping process are presented.
{"title":"Bridging the Gap between bdME and OntoME","authors":"R. G. Martini, P. Henriques","doi":"10.1109/WI.2016.0081","DOIUrl":"https://doi.org/10.1109/WI.2016.0081","url":null,"abstract":"The Semantic Web aims at building a Web where data is enriched with meaningful annotations. In other words, data is semantically organized in such a way that both human and machine can understand and query it, aiming at the creation of dynamic Web pages. Ontologies, as a keystone of the Semantic Web, have gained an ample acceptance as an information model, which can be used for several purposes, such as information retrieval in the Web. However, data is normally stored in databases, which present various problems in the Semantic Web context, because data is not semantically annotated. Aiming at retrieving rich results in the sense of meaning, several ways of relating databases with ontologies have emerged. This paper presents a mapping – with the aid of a framework called Ontop – as a solution for the communication problem between the relational database of the Emigration Museum of Fafe (EMF) and the ontology of the Emigration Museum (OntoME), which describes the Cultural Heritage domain. This mapping will be used to realize the CaVa architecture, aiming at the creation of dynamic Web pages as virtual Learning Spaces. Real examples of the mapping process are presented.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"53 1","pages":"487-491"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75188719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the diversity of alternative programs to watch and the change of viewers' contexts, real-time prediction of viewers' preferences in certain circumstances becomes increasingly hard. However, most existing TV recommender systems used only current time and location in a heuristic way and ignore other contextual information on which viewers' preferences may depend. This paper proposes a probabilistic approach that incorporates contextual information in order to predict the relevance of TV contents. We consider several viewer's current context elements and integrate them into a probabilistic model. We conduct a comprehensive effectiveness evaluation on a real dataset crawled from Pinhole platform. Experimental results demonstrate that our model outperforms the other context-aware models.
{"title":"Exploring Current Viewing Context for TV Contents Recommendation","authors":"Mariem Bambia, M. Boughanem, R. Faiz","doi":"10.1109/WI.2016.0046","DOIUrl":"https://doi.org/10.1109/WI.2016.0046","url":null,"abstract":"Due to the diversity of alternative programs to watch and the change of viewers' contexts, real-time prediction of viewers' preferences in certain circumstances becomes increasingly hard. However, most existing TV recommender systems used only current time and location in a heuristic way and ignore other contextual information on which viewers' preferences may depend. This paper proposes a probabilistic approach that incorporates contextual information in order to predict the relevance of TV contents. We consider several viewer's current context elements and integrate them into a probabilistic model. We conduct a comprehensive effectiveness evaluation on a real dataset crawled from Pinhole platform. Experimental results demonstrate that our model outperforms the other context-aware models.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"94 1","pages":"272-279"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77323749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Personal assistant agents have various abilities to support the user's tasks in the background. They are expected to be run on laptops, portable devices and even on IoT devices. Since their abilities are sometimes restricted by their running environments and hardware, is not always easy for single personal assistance agent to accomplish all the tasks. This paper presents our design and implementation of a cooperative task execution mechanism for cooperative personal assistance agents based on ability ontology.
{"title":"A Cooperative Task Execution Mechanism for Personal Assistant Agents Using Ability Ontology","authors":"Sho Oishi, Naoki Fukuta","doi":"10.1109/WI.2016.0118","DOIUrl":"https://doi.org/10.1109/WI.2016.0118","url":null,"abstract":"Personal assistant agents have various abilities to support the user's tasks in the background. They are expected to be run on laptops, portable devices and even on IoT devices. Since their abilities are sometimes restricted by their running environments and hardware, is not always easy for single personal assistance agent to accomplish all the tasks. This paper presents our design and implementation of a cooperative task execution mechanism for cooperative personal assistance agents based on ability ontology.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"114 1","pages":"664-667"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77650903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maria Bala Duggimpudi, A. Moursy, Elshaimaa Ali, Vijay V. Raghavan
Ontology-based approaches have been explored in several domains for knowledge representation and improving accuracy. However, ontology-based approaches for assisting a decision maker by delivering a concrete plan from analyzing the insights extracted from an ontology, have not received much attention. Insights-as-a-service is a technology that aids a decision maker by providing a concrete action plan, involving a comparative analysis of patterns derived from the data and the extraction of insights from such an analysis. In this paper, we propose an ontology-based architecture for mining insights within the Wireless Network Ontology (WNO), an ontology generated for the wireless network domain for delivering better wireless network performance. We present and illustrate: (i) the major components of the architecture together with the algorithms used for summarizing the network performance profiles in the form of rank tables, and (ii) how the insight rules (the action plan) are extracted from these tables. By utilizing the proposed approach, an actionable plan for assisting the decision maker can be obtained as domain knowledge is incorporated in the system. Experimental results on a wireless network dataset show that the proposed model provides an optimal action plan for a wireless network to improve its performance by encoding data-driven rules into the ontology and suggesting changes to its current network configuration.
{"title":"An Ontology-Based Architecture for Providing Insights in Wireless Networks Domain","authors":"Maria Bala Duggimpudi, A. Moursy, Elshaimaa Ali, Vijay V. Raghavan","doi":"10.1109/WI.2016.0078","DOIUrl":"https://doi.org/10.1109/WI.2016.0078","url":null,"abstract":"Ontology-based approaches have been explored in several domains for knowledge representation and improving accuracy. However, ontology-based approaches for assisting a decision maker by delivering a concrete plan from analyzing the insights extracted from an ontology, have not received much attention. Insights-as-a-service is a technology that aids a decision maker by providing a concrete action plan, involving a comparative analysis of patterns derived from the data and the extraction of insights from such an analysis. In this paper, we propose an ontology-based architecture for mining insights within the Wireless Network Ontology (WNO), an ontology generated for the wireless network domain for delivering better wireless network performance. We present and illustrate: (i) the major components of the architecture together with the algorithms used for summarizing the network performance profiles in the form of rank tables, and (ii) how the insight rules (the action plan) are extracted from these tables. By utilizing the proposed approach, an actionable plan for assisting the decision maker can be obtained as domain knowledge is incorporated in the system. Experimental results on a wireless network dataset show that the proposed model provides an optimal action plan for a wireless network to improve its performance by encoding data-driven rules into the ontology and suggesting changes to its current network configuration.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"22 1","pages":"473-478"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72887052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Florian Geigl, Simon Walk, M. Strohmaier, D. Helic
Ever since the inception of the Web website administrators have tried to steer user browsing behavior for a variety of reasons. For example, to be able to provide the most relevant information, for offering specific products, or to increase revenue from advertisements. One common approach to steer or bias the browsing behavior of users is to influence the link selection process by, for example, highlighting or repositioning links on a website. In this paper, we present a methodology for (i) expressing such navigational biases based on the random surfer model, and for (ii) measuring the consequences of the implemented biases. By adopting a model-based approach we are able to perform a wide range of experiments on seven empirical datasets. Our analyses allows us to gain novel insights into the consequences of navigational biases. Further, we unveil that navigational biases may have significant effects on the browsing processes of users and their typical whereabouts on a website. The first contribution of our work is the formalization of an approach to analyze consequences of navigational biases on the browsing dynamics and visit probabilities of specific pages of a website. Second, we apply this approach to analyze several empirical datasets and improve our understanding of the effects of different biases on real-world websites. In particular, we find that on webgraphs - contrary to undirected networks - typical biases always increase the certainty of the random surfer when selecting a link. Further, we observe significant side effects of biases, which indicate that for practical settings website administrators might need to carefully balance the desired outcomes against undesirable side effects.
{"title":"Steering the Random Surfer on Directed Webgraphs","authors":"Florian Geigl, Simon Walk, M. Strohmaier, D. Helic","doi":"10.1109/WI.2016.0047","DOIUrl":"https://doi.org/10.1109/WI.2016.0047","url":null,"abstract":"Ever since the inception of the Web website administrators have tried to steer user browsing behavior for a variety of reasons. For example, to be able to provide the most relevant information, for offering specific products, or to increase revenue from advertisements. One common approach to steer or bias the browsing behavior of users is to influence the link selection process by, for example, highlighting or repositioning links on a website. In this paper, we present a methodology for (i) expressing such navigational biases based on the random surfer model, and for (ii) measuring the consequences of the implemented biases. By adopting a model-based approach we are able to perform a wide range of experiments on seven empirical datasets. Our analyses allows us to gain novel insights into the consequences of navigational biases. Further, we unveil that navigational biases may have significant effects on the browsing processes of users and their typical whereabouts on a website. The first contribution of our work is the formalization of an approach to analyze consequences of navigational biases on the browsing dynamics and visit probabilities of specific pages of a website. Second, we apply this approach to analyze several empirical datasets and improve our understanding of the effects of different biases on real-world websites. In particular, we find that on webgraphs - contrary to undirected networks - typical biases always increase the certainty of the random surfer when selecting a link. Further, we observe significant side effects of biases, which indicate that for practical settings website administrators might need to carefully balance the desired outcomes against undesirable side effects.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"30 1","pages":"280-287"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73049931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yulong Gu, Jiaxing Song, Weidong Liu, Lixin Zou, Y. Yao
Event-based Social Networks(EBSNs) which combine online interactions and offline events among users have experienced increased popularity and rapid growth recently. In EBSNs, event recommendation is significant for users due to the extremely large amount of events. However, the event recommendation problem is rather challenging because it faces a serious cold-start problem: Events have short life time and new events are registered by only a few users. What's more, there are only implicit feedback information. Existing approaches like collaborative filtering methods are not suitable for this scenario. In this paper, we propose a Context Aware Matrix Factorization model called AlphaMF to tackle with the problem. Specifically, AlphaMF is a unified model that combines the Matrix Factorization model which models implicit feedbacks with the Linear contextual features model which models explicit contextual features. Extensive experiments on a large real-world EBSN dataset demonstrate that the AlphaMF model significantly outperforms state-of-the-art methods by 11%.
{"title":"Context Aware Matrix Factorization for Event Recommendation in Event-Based Social Networks","authors":"Yulong Gu, Jiaxing Song, Weidong Liu, Lixin Zou, Y. Yao","doi":"10.1109/WI.2016.0043","DOIUrl":"https://doi.org/10.1109/WI.2016.0043","url":null,"abstract":"Event-based Social Networks(EBSNs) which combine online interactions and offline events among users have experienced increased popularity and rapid growth recently. In EBSNs, event recommendation is significant for users due to the extremely large amount of events. However, the event recommendation problem is rather challenging because it faces a serious cold-start problem: Events have short life time and new events are registered by only a few users. What's more, there are only implicit feedback information. Existing approaches like collaborative filtering methods are not suitable for this scenario. In this paper, we propose a Context Aware Matrix Factorization model called AlphaMF to tackle with the problem. Specifically, AlphaMF is a unified model that combines the Matrix Factorization model which models implicit feedbacks with the Linear contextual features model which models explicit contextual features. Extensive experiments on a large real-world EBSN dataset demonstrate that the AlphaMF model significantly outperforms state-of-the-art methods by 11%.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"4 1","pages":"248-255"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73767583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mehran Kamkarhaghighi, Iuliia Chepurna, S. Aghababaei, M. Makrehchi
Despite extensive research efforts in stock market predictions using social media networks, there still exists a lack of credible sources of information in such media. This study presents a novel approach to measure the credibility of Twitter users in a domain of interest, namely the stock market. This study suggests a correlation between each user's credibility and the extracted features from each follower network: number of followers, number of stock market-related followers, extracted by a $Cashtag-based approach, ratio of stock market-related followers to the total number of followers, and the number of seed user tweets. The results support the initial hypothesis of this study.
{"title":"Discovering Credible Twitter Users in Stock Market Domain","authors":"Mehran Kamkarhaghighi, Iuliia Chepurna, S. Aghababaei, M. Makrehchi","doi":"10.1109/WI.2016.0020","DOIUrl":"https://doi.org/10.1109/WI.2016.0020","url":null,"abstract":"Despite extensive research efforts in stock market predictions using social media networks, there still exists a lack of credible sources of information in such media. This study presents a novel approach to measure the credibility of Twitter users in a domain of interest, namely the stock market. This study suggests a correlation between each user's credibility and the extracted features from each follower network: number of followers, number of stock market-related followers, extracted by a $Cashtag-based approach, ratio of stock market-related followers to the total number of followers, and the number of seed user tweets. The results support the initial hypothesis of this study.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"13 1","pages":"66-72"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85561835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a personalized recommendation system mining online product reviews, fusing opinions together and providing a ranked order of a set similar products. We define three attributes of opinion summary: opinion coverage, opinion consistency and opinion consensus. Confidence factor is computed based on these attributes. A user specifies the relative importance of each product feature. The quantitive summary reflects the user's preference, the opinion synopsis and the confidence measurement.
{"title":"Personalized Recommendation with Confidence","authors":"Xiaoqing Zhang, Sadhana Kuthuru, Rama Mara, Brahmi Mamillapalli","doi":"10.1109/WI.2016.0099","DOIUrl":"https://doi.org/10.1109/WI.2016.0099","url":null,"abstract":"This paper presents a personalized recommendation system mining online product reviews, fusing opinions together and providing a ranked order of a set similar products. We define three attributes of opinion summary: opinion coverage, opinion consistency and opinion consensus. Confidence factor is computed based on these attributes. A user specifies the relative importance of each product feature. The quantitive summary reflects the user's preference, the opinion synopsis and the confidence measurement.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"30 1","pages":"578-581"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77251323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of healthcare technology and medical standard, the demands of the quality and efficiency of mobile nursing are increased significantly, hence it is necessary to improve hospital nursing mechanism dealing with massive tasks. In this paper, we develop an optimized mobile nursing system based on the specific nurse workflow analysis in obstetrics. An efficient integration system framework is proposed combined with existing hospital common systems and WLAN network environment, which implements automatically execute data interaction. A combination model of C/S using PDA on hand and B/S using PC working at nurse workstation is implemented which ensure the mobility and centrality. A lighten AJAX-SSH2 development framework is used to enhance the function expansibility. The proposed system has been applied in a regional obstetrics hospital in China. Practical clinical application results prove that the proposed system can raise nursing efficiency and reduce medical error rate, make it possible to nurses paying more attention on patients. The healthcare big data collected by this system has considerable value for further research.
{"title":"Development and Application of Mobile Nursing System in Obstetrics","authors":"Ye Yuan, Ke-bin Jia, Zhonghua Sun","doi":"10.1109/WI.2016.0129","DOIUrl":"https://doi.org/10.1109/WI.2016.0129","url":null,"abstract":"With the development of healthcare technology and medical standard, the demands of the quality and efficiency of mobile nursing are increased significantly, hence it is necessary to improve hospital nursing mechanism dealing with massive tasks. In this paper, we develop an optimized mobile nursing system based on the specific nurse workflow analysis in obstetrics. An efficient integration system framework is proposed combined with existing hospital common systems and WLAN network environment, which implements automatically execute data interaction. A combination model of C/S using PDA on hand and B/S using PC working at nurse workstation is implemented which ensure the mobility and centrality. A lighten AJAX-SSH2 development framework is used to enhance the function expansibility. The proposed system has been applied in a regional obstetrics hospital in China. Practical clinical application results prove that the proposed system can raise nursing efficiency and reduce medical error rate, make it possible to nurses paying more attention on patients. The healthcare big data collected by this system has considerable value for further research.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"44 1","pages":"717-720"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80911801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A combination of multiple retrieval systems can outperform its individual component systems, but it remains a challenging problem to predict whether two systems can be beneficially combined and, if so, the optimal means by which they should be merged. The performance of combined systems is affected by many factors, including the performance of individual systems, the diversity between a pair of systems, and the method for combination. In this paper, we undertake the study of these issues using combinatorial fusion algorithm (CFA) utilizing the rank-score characteristic (RSC) function and the notion of a weighted cognitive diversity. Using the selected eight TREC datasets, we demonstrated that: (a) the combination of two retrieval systems performs better than each individual system only when the individual systems have relatively good performance and they are diverse, (b) a dynamic combination method, using rank vs. score combination based on cognitive diversity which does not display a tight correlation with other statistical diversity measures, can improve the performance of the combined system, even when performance of each individual system is not known or in the context of an unsupervised learning environment. Within the TREC datasets, the proposed dynamic approach offers a potential for substantial improvement with no significant risk. Our results provide a new paradigm of dynamic fusion to the study of the combination of multiple retrieval systems.
{"title":"Improved Combination of Multiple Retrieval Systems Using a Dynamic Combinatorial Fusion Algorithm","authors":"Hongzhi Liu, Zhonghai Wu, D. Hsu, B. Kristal","doi":"10.1109/WI.2016.0102","DOIUrl":"https://doi.org/10.1109/WI.2016.0102","url":null,"abstract":"A combination of multiple retrieval systems can outperform its individual component systems, but it remains a challenging problem to predict whether two systems can be beneficially combined and, if so, the optimal means by which they should be merged. The performance of combined systems is affected by many factors, including the performance of individual systems, the diversity between a pair of systems, and the method for combination. In this paper, we undertake the study of these issues using combinatorial fusion algorithm (CFA) utilizing the rank-score characteristic (RSC) function and the notion of a weighted cognitive diversity. Using the selected eight TREC datasets, we demonstrated that: (a) the combination of two retrieval systems performs better than each individual system only when the individual systems have relatively good performance and they are diverse, (b) a dynamic combination method, using rank vs. score combination based on cognitive diversity which does not display a tight correlation with other statistical diversity measures, can improve the performance of the combined system, even when performance of each individual system is not known or in the context of an unsupervised learning environment. Within the TREC datasets, the proposed dynamic approach offers a potential for substantial improvement with no significant risk. Our results provide a new paradigm of dynamic fusion to the study of the combination of multiple retrieval systems.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"1 1","pages":"592-596"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79937158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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