As one of the most popular recommender system technologies, neighborhood-based collaborative filtering algorithm has obtained great favor due to its simplicity, justifiability, and stability. However, when faced with large-scale, sparse, or noise affected data, nearest-neighbor collaborative filtering performs not so well, as the calculation of similarity between user or item pairs is costly and the accuracy of similarity can be easily affected by noise and sparsity. In this paper, we present a novel collaborative filtering method based on user stars. Instead of treating every user as the same, we propose a method to generate a small number of users as the most reliable emph{star users} and then produce predictions for the general population based on star users' ratings. Empirical studies on two different datasets suggest that our method outperforms traditional neighborhood-based collaborative filtering algorithm in terms of both efficiency and accuracy.
{"title":"Collaborative Filtering Based on Star Users","authors":"Qiang Liu, Bingfei Cheng, Congfu Xu","doi":"10.1109/ICTAI.2011.41","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.41","url":null,"abstract":"As one of the most popular recommender system technologies, neighborhood-based collaborative filtering algorithm has obtained great favor due to its simplicity, justifiability, and stability. However, when faced with large-scale, sparse, or noise affected data, nearest-neighbor collaborative filtering performs not so well, as the calculation of similarity between user or item pairs is costly and the accuracy of similarity can be easily affected by noise and sparsity. In this paper, we present a novel collaborative filtering method based on user stars. Instead of treating every user as the same, we propose a method to generate a small number of users as the most reliable emph{star users} and then produce predictions for the general population based on star users' ratings. Empirical studies on two different datasets suggest that our method outperforms traditional neighborhood-based collaborative filtering algorithm in terms of both efficiency and accuracy.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134195060","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}
Z. Assaghir, A. Napoli, Mehdi Kaytoue-Uberall, D. Dubois, H. Prade
The problem addressed in this paper is the merging of numerical information provided by several sources. Merging conflicting pieces of information into an interpretable and useful format is a tricky task even when an information fusion method is chosen. The use of formal concept analysis and pattern structures enables us to associate subsets of sources to combination results obtainable from consistent subsets of pieces of information. This provides a lattice of arguments where the reliability of sources can be taken into account. Instead of providing a unique fusion result, the method yields a structured view of partial results labelled by subsets of sources and allows us to argue about the most appropriate evaluation. The approach is illustrated with an experiment on a real-world application to decision aid in agricultural practices.
{"title":"Numerical Information Fusion: Lattice of Answers with Supporting Arguments","authors":"Z. Assaghir, A. Napoli, Mehdi Kaytoue-Uberall, D. Dubois, H. Prade","doi":"10.1109/ICTAI.2011.98","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.98","url":null,"abstract":"The problem addressed in this paper is the merging of numerical information provided by several sources. Merging conflicting pieces of information into an interpretable and useful format is a tricky task even when an information fusion method is chosen. The use of formal concept analysis and pattern structures enables us to associate subsets of sources to combination results obtainable from consistent subsets of pieces of information. This provides a lattice of arguments where the reliability of sources can be taken into account. Instead of providing a unique fusion result, the method yields a structured view of partial results labelled by subsets of sources and allows us to argue about the most appropriate evaluation. The approach is illustrated with an experiment on a real-world application to decision aid in agricultural practices.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"357 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132700751","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 studies a new evolutionary multiobjective optimization algorithm (EMOA) that leverages quality indicators in parent selection and environmental selection operators. The proposed indicator-based EMOA, called SMSPEMOA, is designed as an extension to SMS-EMOA, which is one of the most successfully and widely used indicator based EMOAs. SMSP-EMOA uses the prospect indicator in its parent selection and the hyper volume indicator in its environmental selection. The prospect indicator measures the potential (or prospect) of each individual to reproduce offspring that dominate itself and spread out in the objective space. It allows the parent selection operator to (1) maintain sufficient selection pressure, even in high dimensional MOPs, thereby improving convergence velocity toward the Pareto-optimal front, and (2) diversify individuals, even in high dimensional MOPs, thereby spreading out individuals in the objective space. Experimental results show that SMSP-EMOA's parent selection operator complement its environmental selection operator. SMSP-EMOA outperforms SMS-EMOA and well-known traditional EMOAs in optimality and convergence velocity without sacrificing the diversity of individuals.
{"title":"SMSP-EMOA: Augmenting SMS-EMOA with the Prospect Indicator for Multiobjective Optimization","authors":"Dung H. Phan, J. Suzuki, P. Boonma","doi":"10.1109/ICTAI.2011.47","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.47","url":null,"abstract":"This paper studies a new evolutionary multiobjective optimization algorithm (EMOA) that leverages quality indicators in parent selection and environmental selection operators. The proposed indicator-based EMOA, called SMSPEMOA, is designed as an extension to SMS-EMOA, which is one of the most successfully and widely used indicator based EMOAs. SMSP-EMOA uses the prospect indicator in its parent selection and the hyper volume indicator in its environmental selection. The prospect indicator measures the potential (or prospect) of each individual to reproduce offspring that dominate itself and spread out in the objective space. It allows the parent selection operator to (1) maintain sufficient selection pressure, even in high dimensional MOPs, thereby improving convergence velocity toward the Pareto-optimal front, and (2) diversify individuals, even in high dimensional MOPs, thereby spreading out individuals in the objective space. Experimental results show that SMSP-EMOA's parent selection operator complement its environmental selection operator. SMSP-EMOA outperforms SMS-EMOA and well-known traditional EMOAs in optimality and convergence velocity without sacrificing the diversity of individuals.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115547748","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}
Information on the World Wide Web is congested with large amounts of news contents. Recommendation, filtering, and summarization of Web news have received much attention in Web intelligence, aiming to find interesting news and summarize concise content for users. In this paper, we present our research on developing the Personalized News Filtering and Summarization system (PNFS). An embedded learning component of PNFS induces a user interest model and recommends personalized news. A keyword knowledge base is maintained and provides a real-time update to reflect the general Web news topic information and the user's interest preferences. The non-news content irrelevant to the news Web page is filtered out. Keywords that capture the main topic of the news are extracted using lexical chains to represent semantic relations between words. An Example run of our PNFS system demonstrates the superiority of this Web intelligence system.
{"title":"Personalized News Filtering and Summarization on the Web","authors":"Xindong Wu, Fei Xie, Gongqing Wu, W. Ding","doi":"10.1109/ICTAI.2011.68","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.68","url":null,"abstract":"Information on the World Wide Web is congested with large amounts of news contents. Recommendation, filtering, and summarization of Web news have received much attention in Web intelligence, aiming to find interesting news and summarize concise content for users. In this paper, we present our research on developing the Personalized News Filtering and Summarization system (PNFS). An embedded learning component of PNFS induces a user interest model and recommends personalized news. A keyword knowledge base is maintained and provides a real-time update to reflect the general Web news topic information and the user's interest preferences. The non-news content irrelevant to the news Web page is filtered out. Keywords that capture the main topic of the news are extracted using lexical chains to represent semantic relations between words. An Example run of our PNFS system demonstrates the superiority of this Web intelligence system.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124168051","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}
Vahid Azadehgan, Alireza Sooni, Nafiseh Jafarian, Deniz Khateri
Hybridization of intelligent techniques, coming from different computational intelligence areas, has become popular because of the growing awareness that such combinations frequently perform better than the individual techniques coming from computational intelligence. Therefore, in this paper a combined model based on honey bees mating optimization and Extremal Optimization has been developed. So it can converge to optimum points faster than before. Simulation and comparisons based on several well-studied and new benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed algorithms.
{"title":"A New Hybrid Algorithm for Multiobjective Optimization","authors":"Vahid Azadehgan, Alireza Sooni, Nafiseh Jafarian, Deniz Khateri","doi":"10.1109/ICTAI.2011.152","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.152","url":null,"abstract":"Hybridization of intelligent techniques, coming from different computational intelligence areas, has become popular because of the growing awareness that such combinations frequently perform better than the individual techniques coming from computational intelligence. Therefore, in this paper a combined model based on honey bees mating optimization and Extremal Optimization has been developed. So it can converge to optimum points faster than before. Simulation and comparisons based on several well-studied and new benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed algorithms.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114865435","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 have proposed a design tool for synthesizing multitier computer networks based on the concept of molecular assembly (MA), where the network's nodes integrate intelligently together by exploiting various forces of attraction existing between the nodes. Three forces are defined and the forces are the distance force, the incoming flow force and the outgoing flow force. Our simulation results demonstrate that for a given unassembled network of 50 nodes, our design tool forms a self-assembled network and manages to reduce the traffic at the backbone by 40% in a short computing time.
{"title":"Molecular Assembly Tool for Synthesizing Multitier Computer Networks","authors":"S. Habib, P. Marimuthu","doi":"10.1109/ICTAI.2011.141","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.141","url":null,"abstract":"We have proposed a design tool for synthesizing multitier computer networks based on the concept of molecular assembly (MA), where the network's nodes integrate intelligently together by exploiting various forces of attraction existing between the nodes. Three forces are defined and the forces are the distance force, the incoming flow force and the outgoing flow force. Our simulation results demonstrate that for a given unassembled network of 50 nodes, our design tool forms a self-assembled network and manages to reduce the traffic at the backbone by 40% in a short computing time.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123653862","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 develop a set of solution techniques for real-time evacuation guidance of pedestrians during emergency, focusing on evacuation from buildings during a fire. We model the problem as an extension of a dynamic network flow by allowing for nodes and edges to expire over time. This captures evacuation situations where the spreading hazard renders parts of the network unavailable. We formally state the problem, analyze its complexity, develop a set of heuristic approaches and compare their performance against a number of most relevant alternative approaches. We experimentally demonstrate that our heuristics outperform the alternatives and are suitable for real-time use even for large networks.
{"title":"Real-Time Pedestrian Evacuation Planning during Emergency","authors":"T. Hadzic, Kenneth N. Brown, C. Sreenan","doi":"10.1109/ICTAI.2011.95","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.95","url":null,"abstract":"We develop a set of solution techniques for real-time evacuation guidance of pedestrians during emergency, focusing on evacuation from buildings during a fire. We model the problem as an extension of a dynamic network flow by allowing for nodes and edges to expire over time. This captures evacuation situations where the spreading hazard renders parts of the network unavailable. We formally state the problem, analyze its complexity, develop a set of heuristic approaches and compare their performance against a number of most relevant alternative approaches. We experimentally demonstrate that our heuristics outperform the alternatives and are suitable for real-time use even for large networks.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121953450","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 propose a scalable algorithm called HiRise2 for incrementally solving soft linear constraints over real domains. It is based on a framework for soft constraints, known as constraint hierarchies, to allow effective modeling of user interface applications by using hierarchical preferences for constraints. HiRise2 introduces LU decompositions to improve the scalability of an incremental simplex method. Using this algorithm, we implemented a constraint solver. We also show the results of experiments on the performance of the solver.
{"title":"A Simplex-Based Scalable Linear Constraint Solver for User Interface Applications","authors":"H. Hosobe","doi":"10.1109/ICTAI.2011.124","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.124","url":null,"abstract":"We propose a scalable algorithm called HiRise2 for incrementally solving soft linear constraints over real domains. It is based on a framework for soft constraints, known as constraint hierarchies, to allow effective modeling of user interface applications by using hierarchical preferences for constraints. HiRise2 introduces LU decompositions to improve the scalability of an incremental simplex method. Using this algorithm, we implemented a constraint solver. We also show the results of experiments on the performance of the solver.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124725312","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}
Imen Saffar, Arnaud Doniec, J. Boonaert, S. Lecoeuche
The multi-agent simulation consists in using a set of interacting agents to reproduce the dynamics and the evolution of the phenomena that we seek to simulate. It is considered now as an alternative to classical simulations based on analytical models. But, its implementation remains difficult, particularly in terms of behaviors extraction and agents modelling. This task is usually performed by the designer who has some expertise and available observation data on the process. In this paper, we propose a novel way to make use of the observations of real world agents to model simulated agents. The modelling is based on clustering techniques. Our approach is illustrated through an example in which the behaviors of agents are extracted as trajectories and destinations from video sequences analysis. This methodology is investigated with the aim to apply it, in particular, in a retail space simulation for the evaluation of marketing strategies. This paper presents experiments of our methodology in the context of a public area modelling.
{"title":"Multi-agent Simulation Design Driven by Real Observations and Clustering Techniques","authors":"Imen Saffar, Arnaud Doniec, J. Boonaert, S. Lecoeuche","doi":"10.1109/ICTAI.2011.89","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.89","url":null,"abstract":"The multi-agent simulation consists in using a set of interacting agents to reproduce the dynamics and the evolution of the phenomena that we seek to simulate. It is considered now as an alternative to classical simulations based on analytical models. But, its implementation remains difficult, particularly in terms of behaviors extraction and agents modelling. This task is usually performed by the designer who has some expertise and available observation data on the process. In this paper, we propose a novel way to make use of the observations of real world agents to model simulated agents. The modelling is based on clustering techniques. Our approach is illustrated through an example in which the behaviors of agents are extracted as trajectories and destinations from video sequences analysis. This methodology is investigated with the aim to apply it, in particular, in a retail space simulation for the evaluation of marketing strategies. This paper presents experiments of our methodology in the context of a public area modelling.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124749881","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}
In this paper we propose FM-PGA, a MapReduce-based hybrid of FM-test and Parallel Genetic Algorithm (PGA), to analyze gene micro array data on cloud computing platform. We investigate the performance of FM-PGA on real-world micro array data and compare it with FM-GA and MapReduce PGA. The experimental results confirm that the genes selected by FM-PGA achieve comparable classification accuracy while the time to achieve that accuracy is significantly reduced.
{"title":"Cloud Parallel Genetic Algorithm for Gene Microarray Data Analysis","authors":"R. A. B. Palomino, L. Liang","doi":"10.1109/ICTAI.2011.160","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.160","url":null,"abstract":"In this paper we propose FM-PGA, a MapReduce-based hybrid of FM-test and Parallel Genetic Algorithm (PGA), to analyze gene micro array data on cloud computing platform. We investigate the performance of FM-PGA on real-world micro array data and compare it with FM-GA and MapReduce PGA. The experimental results confirm that the genes selected by FM-PGA achieve comparable classification accuracy while the time to achieve that accuracy is significantly reduced.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127523847","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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