In this paper, we present a hybrid recommendation approach for discovering potential preferences of individual users. The proposed approach provides a flexible solution that incorporates multidimensional clustering into a collaborative filtering recommendation model to provide a quality recommendation. This facilitates to obtain user clusters which have diverse preference from multi-view for improving effectiveness and diversity of recommendation. The presented algorithm works in three phases: data preprocessing and multidimensional clustering, choosing the appropriate clusters and recommending for the target user. The performance of proposed approach is evaluated using a public movie dataset and compared with two representative recommendation algorithms. The empirical results demonstrate that our proposed approach is likely to trade-off on increasing the diversity of recommendations while maintaining the accuracy of recommendations.
{"title":"Using Multidimensional Clustering Based Collaborative Filtering Approach Improving Recommendation Diversity","authors":"Xiaohui Li, T. Murata","doi":"10.1109/WI-IAT.2012.229","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.229","url":null,"abstract":"In this paper, we present a hybrid recommendation approach for discovering potential preferences of individual users. The proposed approach provides a flexible solution that incorporates multidimensional clustering into a collaborative filtering recommendation model to provide a quality recommendation. This facilitates to obtain user clusters which have diverse preference from multi-view for improving effectiveness and diversity of recommendation. The presented algorithm works in three phases: data preprocessing and multidimensional clustering, choosing the appropriate clusters and recommending for the target user. The performance of proposed approach is evaluated using a public movie dataset and compared with two representative recommendation algorithms. The empirical results demonstrate that our proposed approach is likely to trade-off on increasing the diversity of recommendations while maintaining the accuracy of recommendations.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126117733","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}
Open ontology-described repositories are becoming very common in the web and in enterprises. These repositories are well-suited to answer complex queries, but in order to fully exploit their potential, the queries should be written in a user-demand basis, and not in a traditional static approach by software developers. Hence, the users are required (i) to know the underlying ontology(ies) and/to (ii) write formal queries. Yet, the users often lack such requirements. In this paper we first describe the observations made during manual complex querying process and present a systematization of the users' support wish list for building complex queries. Based on this systematization we propose an extended set of functionalities for a user-supporting system. Finally, we demonstrate their application in a walk-through example and their implementation within a prototype.
{"title":"Enhancing LOD Complex Query Building with Context","authors":"R. Brandão, P. Maio, Nuno Silva","doi":"10.1109/WI-IAT.2012.94","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.94","url":null,"abstract":"Open ontology-described repositories are becoming very common in the web and in enterprises. These repositories are well-suited to answer complex queries, but in order to fully exploit their potential, the queries should be written in a user-demand basis, and not in a traditional static approach by software developers. Hence, the users are required (i) to know the underlying ontology(ies) and/to (ii) write formal queries. Yet, the users often lack such requirements. In this paper we first describe the observations made during manual complex querying process and present a systematization of the users' support wish list for building complex queries. Based on this systematization we propose an extended set of functionalities for a user-supporting system. Finally, we demonstrate their application in a walk-through example and their implementation within a prototype.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125802699","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 proposed a Simple and Fast Multi-Instance Classification Via Support Vector Machine(called Fast MI-SVM). Compared with the other conventional Multi-Instance learning method, our method is able to deal with multi-instance learning problem by only solving a quadratic programming problem. So the training time of Fast MI-SVM is very fast. All numerical experiments on benchmark datasets show the feasibility and validity of the proposed method.
{"title":"A Simple and Fast Multi-instance Classification via Support Vector Machine","authors":"Zhiquan Qi, Ying-jie Tian, Yong Shi","doi":"10.1109/WI-IAT.2012.50","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.50","url":null,"abstract":"In this paper, we proposed a Simple and Fast Multi-Instance Classification Via Support Vector Machine(called Fast MI-SVM). Compared with the other conventional Multi-Instance learning method, our method is able to deal with multi-instance learning problem by only solving a quadratic programming problem. So the training time of Fast MI-SVM is very fast. All numerical experiments on benchmark datasets show the feasibility and validity of the proposed method.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122768177","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}
Wenping Zhang, Raymond Y. K. Lau, Yunqing Xia, Chunping Li, Wenjie Li
Though numerous research has been devoted to social network discovery and analysis, relatively little research has been conducted on business network discovery. The main contribution of our research is the development of a novel probabilistic generative model for latent business networks mining. Our experimental results confirm that the proposed method outperforms the well-known vector space based model by 24% in terms of AUC value.
{"title":"Latent Business Networks Mining: A Probabilistic Generative Model","authors":"Wenping Zhang, Raymond Y. K. Lau, Yunqing Xia, Chunping Li, Wenjie Li","doi":"10.1109/WI-IAT.2012.195","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.195","url":null,"abstract":"Though numerous research has been devoted to social network discovery and analysis, relatively little research has been conducted on business network discovery. The main contribution of our research is the development of a novel probabilistic generative model for latent business networks mining. Our experimental results confirm that the proposed method outperforms the well-known vector space based model by 24% in terms of AUC value.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121866919","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}
The myth that financial trading is an art has been mostly destroyed in the recent decade due to the proliferation of algorithmic trading. In equity markets, algorithmic trading has already bypass human traders in terms of traded volume. This trend seems to be irreversible, and other asset classes are also quickly becoming dominated by the machine traders. However, for asset that requires deeper understanding of physicality, like the trading of commodities, human traders still have significant edge over machines. The primary advantage of human traders in such market is the qualitative expert knowledge that requires traders to consider not just the financial information, but also a wide variety of physical constraints and information. However, due to rapid technology changes and the "invasion" of cash-rich hedge funds, even this traditionally human-centric asset class is crying for help in handling increasingly complicated and volatile environment. In this paper, we propose an adaptive trading support framework that allows us to quantify expert's knowledge to help human traders. Our method is based on a two-state switching Kalman filter, which updates its state estimation continuously with real-time information. We demonstrate the effectiveness of our approach in palm oil trading, which is becoming more and more complicated in recent years due to its new usage in producing biofuel. We show that the two-state switching Kalman filter tuned with expert domain knowledge can effectively reduce prediction errors when compared against traditional single-state econometric models. With a simple back test, we also demonstrate that even a slight decrease in the prediction errors can lead to significant improvement in the trading performance of a naive trading algorithm.
{"title":"Knowledge-Driven Autonomous Commodity Trading Advisor","authors":"Yee Pin Lim, Shih-Fen Cheng","doi":"10.1109/WI-IAT.2012.208","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.208","url":null,"abstract":"The myth that financial trading is an art has been mostly destroyed in the recent decade due to the proliferation of algorithmic trading. In equity markets, algorithmic trading has already bypass human traders in terms of traded volume. This trend seems to be irreversible, and other asset classes are also quickly becoming dominated by the machine traders. However, for asset that requires deeper understanding of physicality, like the trading of commodities, human traders still have significant edge over machines. The primary advantage of human traders in such market is the qualitative expert knowledge that requires traders to consider not just the financial information, but also a wide variety of physical constraints and information. However, due to rapid technology changes and the \"invasion\" of cash-rich hedge funds, even this traditionally human-centric asset class is crying for help in handling increasingly complicated and volatile environment. In this paper, we propose an adaptive trading support framework that allows us to quantify expert's knowledge to help human traders. Our method is based on a two-state switching Kalman filter, which updates its state estimation continuously with real-time information. We demonstrate the effectiveness of our approach in palm oil trading, which is becoming more and more complicated in recent years due to its new usage in producing biofuel. We show that the two-state switching Kalman filter tuned with expert domain knowledge can effectively reduce prediction errors when compared against traditional single-state econometric models. With a simple back test, we also demonstrate that even a slight decrease in the prediction errors can lead to significant improvement in the trading performance of a naive trading algorithm.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131932224","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}
Smart Grid is the trend of next generation electrical power system which makes the power grid intelligent and energy efficient. It requires high level of network reliability to support two-way communication among electrical services, electrical units such as smart meters, and applications. The wireless mesh network infrastructure can provide redundant routes for the smart grid communication network to ensure the network availability. Also due to its high level of flexibility and scalability features that make it become a promising solution for smart grid. However, wireless mesh network infrastructure are vulnerable to some cyber attacks which need to be addressed. In this paper, we proposed and implemented a new trust-based geographical routing protocol, named as Dynamic Trust Elective Geo Routing (DTEGR), which use the trust factor with a dynamic threshold value to generate a trust forwarding list, then it uses distance factor as routing metric to decide the next hop from the trust forwarding list. The simulation studies have confirmed our new DTEGR algorithm able to achieve better routing performance in different network scenarios, and also to achieve high level of reliable data transmission in smart grid communication network.
{"title":"Self-Adjustable Trust-Based Energy Efficient Routing for Smart Grid Systems","authors":"Ming-Sen Xiang, Q. Bai, William Liu","doi":"10.1109/WI-IAT.2012.89","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.89","url":null,"abstract":"Smart Grid is the trend of next generation electrical power system which makes the power grid intelligent and energy efficient. It requires high level of network reliability to support two-way communication among electrical services, electrical units such as smart meters, and applications. The wireless mesh network infrastructure can provide redundant routes for the smart grid communication network to ensure the network availability. Also due to its high level of flexibility and scalability features that make it become a promising solution for smart grid. However, wireless mesh network infrastructure are vulnerable to some cyber attacks which need to be addressed. In this paper, we proposed and implemented a new trust-based geographical routing protocol, named as Dynamic Trust Elective Geo Routing (DTEGR), which use the trust factor with a dynamic threshold value to generate a trust forwarding list, then it uses distance factor as routing metric to decide the next hop from the trust forwarding list. The simulation studies have confirmed our new DTEGR algorithm able to achieve better routing performance in different network scenarios, and also to achieve high level of reliable data transmission in smart grid communication network.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130428929","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}
The task of sentiment classification relies heavily on sentiment resources, including annotated lexicons and corpus. However, the sentiment resources in different languages are imbalanced. In particular, many reliable English resources are available on the Web, while reliable Chinese resources are scarce till now. Cross-lingual sentiment classification is a promising way for addressing the above problem by leveraging only English resources for Chinese sentiment classification. In this study, we conduct a comparative study to explore the challenges of cross-lingual sentiment classification. Different schemes for cross-lingual sentiment classification based on two dimensions have been compared empirically. Lastly, we propose to combine the different individual schemes into an ensemble. Experiment results demonstrate the effectiveness of the proposed method.
{"title":"A Comparative Study of Cross-Lingual Sentiment Classification","authors":"Xiaojun Wan","doi":"10.1109/WI-IAT.2012.54","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.54","url":null,"abstract":"The task of sentiment classification relies heavily on sentiment resources, including annotated lexicons and corpus. However, the sentiment resources in different languages are imbalanced. In particular, many reliable English resources are available on the Web, while reliable Chinese resources are scarce till now. Cross-lingual sentiment classification is a promising way for addressing the above problem by leveraging only English resources for Chinese sentiment classification. In this study, we conduct a comparative study to explore the challenges of cross-lingual sentiment classification. Different schemes for cross-lingual sentiment classification based on two dimensions have been compared empirically. Lastly, we propose to combine the different individual schemes into an ensemble. Experiment results demonstrate the effectiveness of the proposed method.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116546808","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}
Tagging is nowadays the most predominant technique to make resources searchable. These allow users to create and manage tags to annotate and categorize content. In this paper, we propose an approach to tag images in a user's collection based upon user's personal profile, his/her social context and the context defined by his/her prior image collection. We apply LDA for context modeling. In this scheme, tag similarity and tag relevance are jointly estimated so that they can profit from each other. We have used an Adaptive Context Model created from user related sources to tag images. Experimental validation with user's mobile as well as website based image collection has established effectiveness of the approach.
{"title":"A Ubiquitous Image Tagging System Using User Context","authors":"Shatabdi Kundu, S. Chaudhury","doi":"10.1109/WI-IAT.2012.249","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.249","url":null,"abstract":"Tagging is nowadays the most predominant technique to make resources searchable. These allow users to create and manage tags to annotate and categorize content. In this paper, we propose an approach to tag images in a user's collection based upon user's personal profile, his/her social context and the context defined by his/her prior image collection. We apply LDA for context modeling. In this scheme, tag similarity and tag relevance are jointly estimated so that they can profit from each other. We have used an Adaptive Context Model created from user related sources to tag images. Experimental validation with user's mobile as well as website based image collection has established effectiveness of the approach.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130296996","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}
Multi-item negotiations surround our daily life and usually involve two parties that share common or conflicting interests. Effective automated negotiation techniques should enable the agents to adaptively adjust their behaviors depending on the characteristics of their negotiating partners and negotiation scenarios. This is complicated by the fact that the negotiation agents are usually unwilling to reveal their information (strategies and preferences) to avoid being exploited during negotiation. In this paper, we propose an adaptive negotiation strategy, called ABiNeS, which can make effective negotiations against different types of negotiating partners. The ABiNeS agent employs the non-exploitation point to adaptively adjust the appropriate time to stop exploiting the negotiating partner and also predicts the optimal offer for the negotiating partner based on reinforcement-learning based approach. Simulation results show that the ABiNeS agent can perform more efficient exploitations against different negotiating partners, and thus achieve higher overall utilities compared with the state-of-the-art negotiation strategies in different negotiation scenarios.
{"title":"ABiNeS: An Adaptive Bilateral Negotiating Strategy over Multiple Items","authors":"Jianye Hao, Ho-fung Leung","doi":"10.1109/WI-IAT.2012.72","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.72","url":null,"abstract":"Multi-item negotiations surround our daily life and usually involve two parties that share common or conflicting interests. Effective automated negotiation techniques should enable the agents to adaptively adjust their behaviors depending on the characteristics of their negotiating partners and negotiation scenarios. This is complicated by the fact that the negotiation agents are usually unwilling to reveal their information (strategies and preferences) to avoid being exploited during negotiation. In this paper, we propose an adaptive negotiation strategy, called ABiNeS, which can make effective negotiations against different types of negotiating partners. The ABiNeS agent employs the non-exploitation point to adaptively adjust the appropriate time to stop exploiting the negotiating partner and also predicts the optimal offer for the negotiating partner based on reinforcement-learning based approach. Simulation results show that the ABiNeS agent can perform more efficient exploitations against different negotiating partners, and thus achieve higher overall utilities compared with the state-of-the-art negotiation strategies in different negotiation scenarios.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133794194","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 present an agent architecture and a hybrid behavior learning method for it that allows the use of communicated intentions of other agents to create agents that are able to cooperate with various configurations of other agents in fulfilling a task. Our shout-ahead architecture is based on two rule sets, one making decisions without communicated intentions and one with these intentions. Reinforcement learning is used to determine in a particular situation which set is responsible for the final decision. Evolutionary learning is used to learn these rules. Our application of this approach to learning behaviors for units in a computer game shows that the use of shout-ahead substantially improves the quality of the learned behavior compared to agents not using shout-ahead.
{"title":"A Hybrid Cooperative Behavior Learning Method for a Rule-Based Shout-Ahead Architecture","authors":"Sanjeev Paskaradevan, J. Denzinger","doi":"10.1109/WI-IAT.2012.33","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.33","url":null,"abstract":"We present an agent architecture and a hybrid behavior learning method for it that allows the use of communicated intentions of other agents to create agents that are able to cooperate with various configurations of other agents in fulfilling a task. Our shout-ahead architecture is based on two rule sets, one making decisions without communicated intentions and one with these intentions. Reinforcement learning is used to determine in a particular situation which set is responsible for the final decision. Evolutionary learning is used to learn these rules. Our application of this approach to learning behaviors for units in a computer game shows that the use of shout-ahead substantially improves the quality of the learned behavior compared to agents not using shout-ahead.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129747779","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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