In the negotiation literature we find two relatively distinct types of negotiation. The two types are known as integrative negotiations and distributive negotiations. Integrative negotiations are those where all sides are looking for solutions that are "good" for everyone while distributive negotiations are those where each party tries to maximize his gain. In this paper we are interested in argumentation- based integrative negotiations. More precisely we present a study characterizing the outcomes of such negotiations. For this reason, we aggregate the argumentation systems that the agents use in order to negotiate. The aggregate argumentation system represents the negotiation theory of the agents as a group and corresponds to the "ideal" situation of having access to complete information or negotiating through a mediator. We show that the aggregation operator we use is very suitable for capturing the essence of integrative negotiation as the outcomes of the aggregate theory we obtain have many appealing properties (e.g. they are Pareto optimal solutions).
{"title":"Characterizing the Outcomes of Argumentation-Based Integrative Negotiation","authors":"Yannis Dimopoulos, Pavlos Moraitis, Leila Amgoud","doi":"10.1109/WIIAT.2008.347","DOIUrl":"https://doi.org/10.1109/WIIAT.2008.347","url":null,"abstract":"In the negotiation literature we find two relatively distinct types of negotiation. The two types are known as integrative negotiations and distributive negotiations. Integrative negotiations are those where all sides are looking for solutions that are \"good\" for everyone while distributive negotiations are those where each party tries to maximize his gain. In this paper we are interested in argumentation- based integrative negotiations. More precisely we present a study characterizing the outcomes of such negotiations. For this reason, we aggregate the argumentation systems that the agents use in order to negotiate. The aggregate argumentation system represents the negotiation theory of the agents as a group and corresponds to the \"ideal\" situation of having access to complete information or negotiating through a mediator. We show that the aggregation operator we use is very suitable for capturing the essence of integrative negotiation as the outcomes of the aggregate theory we obtain have many appealing properties (e.g. they are Pareto optimal solutions).","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124588708","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}
We proposed a cognition-driven decision process model for business intelligence. In this model, a managerpsilas situation awareness (SA) and mental models are developed and enriched for naturalistic decision making based on traditional business intelligence systems. Mental models are also used to supervise the process of situation information retrieval and presentation. The final decision-making process is based on recognition-primed decision model.
{"title":"A Model of Cognition-Driven Decision Process for Business Intelligence","authors":"Li Niu, Guangquan Zhang","doi":"10.1109/WIIAT.2008.216","DOIUrl":"https://doi.org/10.1109/WIIAT.2008.216","url":null,"abstract":"We proposed a cognition-driven decision process model for business intelligence. In this model, a managerpsilas situation awareness (SA) and mental models are developed and enriched for naturalistic decision making based on traditional business intelligence systems. Mental models are also used to supervise the process of situation information retrieval and presentation. The final decision-making process is based on recognition-primed decision model.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114573663","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}
Coalition formation in social networks allows many choices of which task to select and with whom to partner in the social network. Agents communicate with agents within n network links in their surrounding network. These agents are considered part of an agentpsilas local neighborhood. Agents maintain a database of skills possessed by agents in their local neighborhood. We compare agents of two different types. Structural agents seek to create a scale-free network. Inventory agents seek to connect to agents who possess a skill not found in their current local neighborhood. We examine the ability of the agents to deal with static skill demand patterns, changing skill demand patterns, and a mismatch of the skills supplied to the skills demanded.
{"title":"Adapting to Changing Resource Requirements for Coalition Formation in Self-Organized Social Networks","authors":"Levi Barton, V. Allan","doi":"10.1109/WIIAT.2008.121","DOIUrl":"https://doi.org/10.1109/WIIAT.2008.121","url":null,"abstract":"Coalition formation in social networks allows many choices of which task to select and with whom to partner in the social network. Agents communicate with agents within n network links in their surrounding network. These agents are considered part of an agentpsilas local neighborhood. Agents maintain a database of skills possessed by agents in their local neighborhood. We compare agents of two different types. Structural agents seek to create a scale-free network. Inventory agents seek to connect to agents who possess a skill not found in their current local neighborhood. We examine the ability of the agents to deal with static skill demand patterns, changing skill demand patterns, and a mismatch of the skills supplied to the skills demanded.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115041559","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}
Social networks are very popular nowadays and the understanding of their inner structure seems to be a promising area. Several approaches for the social network structure visualization has been introduced and the typical problem is the schematic value of such visualization and the computational complexity of their analysis. This paper proposes a method by using the relation between objects, which is reduced and used as an input to Formal Concept Analysis methods. Our method attempts to deal with both mentioned problems.
{"title":"Understanding Social Networks Using Formal Concept Analysis","authors":"V. Snás̃el, Z. Horak, A. Abraham","doi":"10.1109/WIIAT.2008.74","DOIUrl":"https://doi.org/10.1109/WIIAT.2008.74","url":null,"abstract":"Social networks are very popular nowadays and the understanding of their inner structure seems to be a promising area. Several approaches for the social network structure visualization has been introduced and the typical problem is the schematic value of such visualization and the computational complexity of their analysis. This paper proposes a method by using the relation between objects, which is reduced and used as an input to Formal Concept Analysis methods. Our method attempts to deal with both mentioned problems.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115110975","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}
An online question answering (QA) portal provides users a way to socialize and help each other to solve problems. The majority of the online question answer systems use user-feedback to rank userspsila answers. This way of ranking is inefficient as it involves ongoing efforts by the users and is subjective. Currently researchers have utilized link analysis of user interactions for this task. However, this is not accurate in some circumstances. A detailed structural analysis of an online QA portal is conducted in this paper. A novel approach based on userspsila reputation reflecting the usage patterns is proposed to rank and recommend the user answers. The method is compared with a popular link topology analysis method, HITS. The result of the proposed method is promising.
{"title":"Expertise Analysis in a Question Answer Portal for Author Ranking","authors":"Lin Chen, R. Nayak","doi":"10.1109/WIIAT.2008.12","DOIUrl":"https://doi.org/10.1109/WIIAT.2008.12","url":null,"abstract":"An online question answering (QA) portal provides users a way to socialize and help each other to solve problems. The majority of the online question answer systems use user-feedback to rank userspsila answers. This way of ranking is inefficient as it involves ongoing efforts by the users and is subjective. Currently researchers have utilized link analysis of user interactions for this task. However, this is not accurate in some circumstances. A detailed structural analysis of an online QA portal is conducted in this paper. A novel approach based on userspsila reputation reflecting the usage patterns is proposed to rank and recommend the user answers. The method is compared with a popular link topology analysis method, HITS. The result of the proposed method is promising.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117337904","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}
Shaghayegh Sherry Sahebi, F. Oroumchian, R. Khosravi
The need for recommendation systems to ease user navigations has become evident by growth of information on the Web. There exist many approaches of learning for Web usage-based recommendation systems. In hybrid recommendation systems, other knowledge resources, like content, semantics, and hyperlink structure of the Web site, have been utilized to enhance usage-based personalization systems. In this study, we introduce a new structure-based similarity measure for user sessions. We also apply two clustering algorithms on this similarity measure to compare it to cosine and another structure-based similarity measures. Our experiments exhibit that adding structure information, leveraging the proposed similarity measure, enhances the quality of recommendations in both methods.
{"title":"An Enhanced Similarity Measure for Utilizing Site Structure in Web Personalization Systems","authors":"Shaghayegh Sherry Sahebi, F. Oroumchian, R. Khosravi","doi":"10.1109/WIIAT.2008.270","DOIUrl":"https://doi.org/10.1109/WIIAT.2008.270","url":null,"abstract":"The need for recommendation systems to ease user navigations has become evident by growth of information on the Web. There exist many approaches of learning for Web usage-based recommendation systems. In hybrid recommendation systems, other knowledge resources, like content, semantics, and hyperlink structure of the Web site, have been utilized to enhance usage-based personalization systems. In this study, we introduce a new structure-based similarity measure for user sessions. We also apply two clustering algorithms on this similarity measure to compare it to cosine and another structure-based similarity measures. Our experiments exhibit that adding structure information, leveraging the proposed similarity measure, enhances the quality of recommendations in both methods.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116163643","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}
News Topics are related to a set of keywords or keyphrases. Topic keyphrases briefly describe the key content of topics and help users decide whether to do further reading about them. Moreover, keyphrases of a news topic can be considered as a cluster of related terms, which provides term relationship information that can be integrated into information retrieval models. In this paper, an automatic online news topic keyphrase extraction system is proposed. News stories are organized into topics. Keyword candidates are firstly extracted from single news stories and filtered with topic information. Then a phrase identification process combines keywords into phrases using position information. Finally, the phrases are ranked and top ones are selected as topic keyphrases. Experiments performed on practical Web datasets show that the proposed system works effectively, with a performance of precision=70.61% and recall=67.94%.
{"title":"An Automatic Online News Topic Keyphrase Extraction System","authors":"Canhui Wang, Min Zhang, Liyun Ru, Shaoping Ma","doi":"10.1109/WIIAT.2008.225","DOIUrl":"https://doi.org/10.1109/WIIAT.2008.225","url":null,"abstract":"News Topics are related to a set of keywords or keyphrases. Topic keyphrases briefly describe the key content of topics and help users decide whether to do further reading about them. Moreover, keyphrases of a news topic can be considered as a cluster of related terms, which provides term relationship information that can be integrated into information retrieval models. In this paper, an automatic online news topic keyphrase extraction system is proposed. News stories are organized into topics. Keyword candidates are firstly extracted from single news stories and filtered with topic information. Then a phrase identification process combines keywords into phrases using position information. Finally, the phrases are ranked and top ones are selected as topic keyphrases. Experiments performed on practical Web datasets show that the proposed system works effectively, with a performance of precision=70.61% and recall=67.94%.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116291646","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}
Supporting learners in inclusive lifelong learning scenarios requires a dynamic support that takes into account their learning needs and preferences. This research is focused on an open standard-based recommender system, covering the full life cycle of eLearning. The recommending system I am developing is supported by a multi-agent architecture and its ultimate goal is to improve the learning efficiency and the learnerspsila satisfaction during the execution of the course tasks.
{"title":"Recommending in Inclusive Lifelong Learning Scenarios: Identifying and Managing Runtime Situations","authors":"O. Santos","doi":"10.1109/WIIAT.2008.251","DOIUrl":"https://doi.org/10.1109/WIIAT.2008.251","url":null,"abstract":"Supporting learners in inclusive lifelong learning scenarios requires a dynamic support that takes into account their learning needs and preferences. This research is focused on an open standard-based recommender system, covering the full life cycle of eLearning. The recommending system I am developing is supported by a multi-agent architecture and its ultimate goal is to improve the learning efficiency and the learnerspsila satisfaction during the execution of the course tasks.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121895573","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}
One of the cornerstones of the Social Web is informal user-generated metadata (or tags) for annotating web objects like pages, images, and videos. However, many real-world domains are currently left out of the social tagging phenomenon due to the lack of a wide-scale tagging-savvy audience - domains like the personal desktop, enterprise intranets, and digital libraries. Hence in this paper, we propose a lightweight interactive tagging framework for providing high-quality tag suggestions for the vast majority of untagged content. One of the salient features of the proposed framework is its incorporation of user feedback for iteratively refining tag suggestions. Concretely, we describe and evaluate three feedback models - Tag-Based, Term-Based, and Tag Co-location. Through extensive user evaluation and testing, we find that feedback can significantly improve tag quality with minimal user involvement.
{"title":"Exploring Feedback Models in Interactive Tagging","authors":"R. Graham, James Caverlee","doi":"10.1109/WIIAT.2008.419","DOIUrl":"https://doi.org/10.1109/WIIAT.2008.419","url":null,"abstract":"One of the cornerstones of the Social Web is informal user-generated metadata (or tags) for annotating web objects like pages, images, and videos. However, many real-world domains are currently left out of the social tagging phenomenon due to the lack of a wide-scale tagging-savvy audience - domains like the personal desktop, enterprise intranets, and digital libraries. Hence in this paper, we propose a lightweight interactive tagging framework for providing high-quality tag suggestions for the vast majority of untagged content. One of the salient features of the proposed framework is its incorporation of user feedback for iteratively refining tag suggestions. Concretely, we describe and evaluate three feedback models - Tag-Based, Term-Based, and Tag Co-location. Through extensive user evaluation and testing, we find that feedback can significantly improve tag quality with minimal user involvement.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125962922","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}
Much early evaluation work focused specifically on the "accuracy" of recommendation algorithms. Good recommendation (in terms of accuracy) has, however, to be coupled with other considerations. This work suggests measures aiming at evaluating other aspects than accuracy of recommendation algorithms. Other considerations include (1) coverage, which measures the percentage of a data set that a recommender system is able to provide recommendation for, (2) confidence metrics that can help users make more effective decisions, (3) computing time, which measures how quickly an algorithm can produce good recommendations, (4) novelty/serendipity, which measure whether a recommendation is original, and (5) robustness which measure the ability of the algorithm to make good predictions in the presence of noisy or sparse data. Six collaborative recommendation methods are investigated. Results on artificial data sets (for robustness) or on the real MovieLens data set (for accuracy, novelty, and computing time) are included and analyzed, showing that kernel-based algorithms provide the best results overall.
{"title":"Evaluating Performance of Recommender Systems: An Experimental Comparison","authors":"François Fouss, M. Saerens","doi":"10.1109/WIIAT.2008.252","DOIUrl":"https://doi.org/10.1109/WIIAT.2008.252","url":null,"abstract":"Much early evaluation work focused specifically on the \"accuracy\" of recommendation algorithms. Good recommendation (in terms of accuracy) has, however, to be coupled with other considerations. This work suggests measures aiming at evaluating other aspects than accuracy of recommendation algorithms. Other considerations include (1) coverage, which measures the percentage of a data set that a recommender system is able to provide recommendation for, (2) confidence metrics that can help users make more effective decisions, (3) computing time, which measures how quickly an algorithm can produce good recommendations, (4) novelty/serendipity, which measure whether a recommendation is original, and (5) robustness which measure the ability of the algorithm to make good predictions in the presence of noisy or sparse data. Six collaborative recommendation methods are investigated. Results on artificial data sets (for robustness) or on the real MovieLens data set (for accuracy, novelty, and computing time) are included and analyzed, showing that kernel-based algorithms provide the best results overall.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124676977","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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