Online reviews, which are getting increasingly prevalent with the rapid growth of Web 2.0, have been shown to be second only to ``word-of-mouth'' in terms of influencing purchase decisions. It is therefore imperative to analyze them and distill useful knowledge that could be of economic values to vendors and other interested parties. Previous studies have confirmed that the sentiments expressed in the online reviews are strongly correlated with the sales performance of products. In particular, a model called ARSA has been proposed for predicting sales performance using a model called S-PLSA. In this paper, we build upon that work, and present an adaptive sentiment analysis model called S-PLSA+, which not only can capture the hidden sentiment factors in the reviews, but has the capability to be incrementally updated as more data become available. We show how the proposed S-PLSA+ model can be applied to sales performance prediction using the ARSA model. A case study is conducted in the movie domain, and results from preliminary experiments confirm the effectiveness of the proposed model.
{"title":"An Adaptive Model for Probabilistic Sentiment Analysis","authors":"Xiaohui Yu, Yang Liu, Aijun An","doi":"10.1109/WI-IAT.2010.284","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.284","url":null,"abstract":"Online reviews, which are getting increasingly prevalent with the rapid growth of Web 2.0, have been shown to be second only to ``word-of-mouth'' in terms of influencing purchase decisions. It is therefore imperative to analyze them and distill useful knowledge that could be of economic values to vendors and other interested parties. Previous studies have confirmed that the sentiments expressed in the online reviews are strongly correlated with the sales performance of products. In particular, a model called ARSA has been proposed for predicting sales performance using a model called S-PLSA. In this paper, we build upon that work, and present an adaptive sentiment analysis model called S-PLSA+, which not only can capture the hidden sentiment factors in the reviews, but has the capability to be incrementally updated as more data become available. We show how the proposed S-PLSA+ model can be applied to sales performance prediction using the ARSA model. A case study is conducted in the movie domain, and results from preliminary experiments confirm the effectiveness of the proposed model.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126684254","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}
Communication is a natural way to improve coordination in multi-agent systems under decentralized control. It allows the agents to exchange local information, to increase their observability on the system and thus leading to higher performance. Recent works dealing with decentralized control in cooperative multiagent systems have shown a great interest in Decentralized Markov Decision Processes (DEC-MDPs). However, communication models that are proposed in DEC-MDPs make strong assumptions which seldom hold in realistic multiagent systems where the execution of the agents may be asynchronous, communication is time and resource consuming and may be restricted by temporal constraints. In this paper we propose an approach that allows us to formalize more complex and realistic communication decisions in DEC-MDPs with interaction graph. We assume a communication model where, at each decision step, each agent must be able to decide to communicate or not, which information to communicate and to whom. In order to make such decisions, we extend one of the most scalable decentralized decision model, the DEC-MDP with opportunity cost (OC-DEC-MDP). This new decision model allows us to assess the value of making decisions on when and what communicating and to whom, and to save the performance of OC-DEC-MDPs.
{"title":"A Rich Communication Model in Opportunistic Decentralized Decision Making","authors":"A. Beynier, A. Mouaddib","doi":"10.1109/WI-IAT.2010.41","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.41","url":null,"abstract":"Communication is a natural way to improve coordination in multi-agent systems under decentralized control. It allows the agents to exchange local information, to increase their observability on the system and thus leading to higher performance. Recent works dealing with decentralized control in cooperative multiagent systems have shown a great interest in Decentralized Markov Decision Processes (DEC-MDPs). However, communication models that are proposed in DEC-MDPs make strong assumptions which seldom hold in realistic multiagent systems where the execution of the agents may be asynchronous, communication is time and resource consuming and may be restricted by temporal constraints. In this paper we propose an approach that allows us to formalize more complex and realistic communication decisions in DEC-MDPs with interaction graph. We assume a communication model where, at each decision step, each agent must be able to decide to communicate or not, which information to communicate and to whom. In order to make such decisions, we extend one of the most scalable decentralized decision model, the DEC-MDP with opportunity cost (OC-DEC-MDP). This new decision model allows us to assess the value of making decisions on when and what communicating and to whom, and to save the performance of OC-DEC-MDPs.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"1000 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123324479","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 emergence of massive social media, massive social networks have led to a huge interest in data analysis. In this paper, we propose an empirical study on several massive social networks including 4 mobile call graphs, a fixed-line call graph, two co-authorship networks and two Email networks. We find that call graphs tend to be more locality than the co-authorship networks and Email networks. To our surprise, we even find that there is no significant relations between community sizes and their quality scores for most extracted communities. We also find that some very huge community with high mean quality values, and we can not find the universal ``V'' shape in their mean quality values.
{"title":"Empirical Analysis and Multiple Level Views in Massive Social Networks","authors":"Qi Ye, Bin Wu, Yuan Gao, Bai Wang","doi":"10.1109/WI-IAT.2010.48","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.48","url":null,"abstract":"With the emergence of massive social media, massive social networks have led to a huge interest in data analysis. In this paper, we propose an empirical study on several massive social networks including 4 mobile call graphs, a fixed-line call graph, two co-authorship networks and two Email networks. We find that call graphs tend to be more locality than the co-authorship networks and Email networks. To our surprise, we even find that there is no significant relations between community sizes and their quality scores for most extracted communities. We also find that some very huge community with high mean quality values, and we can not find the universal ``V'' shape in their mean quality values.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121586324","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 investigate the problem of reinforcement learning (RL) in a challenging object-oriented environment, where the functional diversity of objects is high, and the agent must learn quickly by generalizing its experience to novel situations. We present a novel two-layer architecture, which can achieve efficient learning of value function for such environments. The algorithm is implemented by integrating an unsupervised, hierarchical clustering component into the Soar cognitive architecture. Our system coherently incorporates several principles in machine learning and knowledge representation including: dimension reduction, competitive learning, hierarchical representation and sparse coding. We also explore the types of prior domain knowledge that can be used to regulate learning based on the characteristics of environment. The system is empirically evaluated in an artificial domain consisting of interacting objects with diverse functional properties and multiple functional roles. The results demonstrate that the flexibility of hierarchical representation naturally integrates with our novel value function approximation scheme and together they can significantly improve the speed of RL.
{"title":"Efficient Value Function Approximation with Unsupervised Hierarchical Categorization for a Reinforcement Learning Agent","authors":"Yongjia Wang, John E. Laird","doi":"10.1109/WI-IAT.2010.16","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.16","url":null,"abstract":"We investigate the problem of reinforcement learning (RL) in a challenging object-oriented environment, where the functional diversity of objects is high, and the agent must learn quickly by generalizing its experience to novel situations. We present a novel two-layer architecture, which can achieve efficient learning of value function for such environments. The algorithm is implemented by integrating an unsupervised, hierarchical clustering component into the Soar cognitive architecture. Our system coherently incorporates several principles in machine learning and knowledge representation including: dimension reduction, competitive learning, hierarchical representation and sparse coding. We also explore the types of prior domain knowledge that can be used to regulate learning based on the characteristics of environment. The system is empirically evaluated in an artificial domain consisting of interacting objects with diverse functional properties and multiple functional roles. The results demonstrate that the flexibility of hierarchical representation naturally integrates with our novel value function approximation scheme and together they can significantly improve the speed of RL.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122938629","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}
Distributed load managers exhibit thrashing where tasks are repeatedly moved between locations due to incomplete global load information. This paper shows that systems of Autonomous Mobile Programs (AMPs) exhibit the same behaviour, identifying two types of redundant movement and terming them greedy effects. AMPs are unusual in that, in place of some external load management system, each AMP periodically recalculates network and program parameters and may independently move to a better execution environment. Load management emerges from the behaviour of collections of AMPs. The paper explores the extent of greedy effects by simulation, and then proposes negotiating AMPs (NAMPs) to ameliorate the problem. We present the design of AMPs with a competitive negotiation scheme (cNAMPs), and compare their performance with AMPs by simulation.
{"title":"Using Negotiation to Reduce Redundant Autonomous Mobile Program Movements","authors":"Natalia Chechina, P. King, P. Trinder","doi":"10.1109/WI-IAT.2010.22","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.22","url":null,"abstract":"Distributed load managers exhibit thrashing where tasks are repeatedly moved between locations due to incomplete global load information. This paper shows that systems of Autonomous Mobile Programs (AMPs) exhibit the same behaviour, identifying two types of redundant movement and terming them greedy effects. AMPs are unusual in that, in place of some external load management system, each AMP periodically recalculates network and program parameters and may independently move to a better execution environment. Load management emerges from the behaviour of collections of AMPs. The paper explores the extent of greedy effects by simulation, and then proposes negotiating AMPs (NAMPs) to ameliorate the problem. We present the design of AMPs with a competitive negotiation scheme (cNAMPs), and compare their performance with AMPs by simulation.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"35 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120989541","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}
Effectively guiding people in complex and highly dynamic work environment requires advances in high-level declarative activity models that can describe the flow of human work activities and their intended outcomes, as well as novel user interface models for distributing guidance information across time and space. This paper describes a new line of research aimed at developing a new programming and human interface approach for pervasive systems based on high-level models of human activities, so-called situated flows, and mobile projector interfaces for uncovering task information embedded in physical environments
{"title":"Designing Pervasive Interactions for Ambient Guidance with Situated Flows","authors":"F. Kawsar, Gerd Kortuem, Bashar Altakrouri","doi":"10.1109/WI-IAT.2010.119","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.119","url":null,"abstract":"Effectively guiding people in complex and highly dynamic work environment requires advances in high-level declarative activity models that can describe the flow of human work activities and their intended outcomes, as well as novel user interface models for distributing guidance information across time and space. This paper describes a new line of research aimed at developing a new programming and human interface approach for pervasive systems based on high-level models of human activities, so-called situated flows, and mobile projector interfaces for uncovering task information embedded in physical environments","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121233047","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}
Expert Collaborative Filtering is an approach to recommender systems in which recommendations for users are derived from ratings coming from domain experts rather than peers. In this paper we present an implementation of this approach in the music domain. We show the applicability of the model in this setting, and show how it addresses many of the shortcomings in traditional Collaborative Filtering such as possible privacy concerns. We also describe a number of technologies and an architectural solution based on REST and the use of Linked Data that can be used to implement a completely distributed and privacy-preserving recommender system.
{"title":"Towards Fully Distributed and Privacy-Preserving Recommendations via Expert Collaborative Filtering and RESTful Linked Data","authors":"Jae-wook Ahn, X. Amatriain","doi":"10.1109/WI-IAT.2010.53","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.53","url":null,"abstract":"Expert Collaborative Filtering is an approach to recommender systems in which recommendations for users are derived from ratings coming from domain experts rather than peers. In this paper we present an implementation of this approach in the music domain. We show the applicability of the model in this setting, and show how it addresses many of the shortcomings in traditional Collaborative Filtering such as possible privacy concerns. We also describe a number of technologies and an architectural solution based on REST and the use of Linked Data that can be used to implement a completely distributed and privacy-preserving recommender system.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124943324","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}
Expertise matching, aiming to find the alignment between experts and queries, is a common problem in many real applications such as conference paper-reviewer assignment, product-reviewer alignment, and product-endorser matching. Most of existing methods for this problem usually find “relevant” experts for each query independently by using, e.g., an information retrieval method. However, in real-world systems, various domain-specific constraints must be considered. For example, to review a paper, it is desirable that there is at least one senior reviewer to guide the reviewing process. An important question is: “Can we design a framework to efficiently find the optimal solution for expertise matching under various constraints?” This paper explores such an approach by formulating the expertise matching problem in a constraint based optimization framework. Interestingly, the problem can be linked to a convex cost flow problem, which guarantees an optimal solution under given constraints. We also present an online matching algorithm to support incorporating user feedbacks in real time. The proposed approach has been evaluated on two different genres of expertise matching problems. Experimental results validate the effectiveness of the proposed approach.
{"title":"Expertise Matching via Constraint-Based Optimization","authors":"Wenbin Tang, Jie Tang, Chenhao Tan","doi":"10.1109/WI-IAT.2010.133","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.133","url":null,"abstract":"Expertise matching, aiming to find the alignment between experts and queries, is a common problem in many real applications such as conference paper-reviewer assignment, product-reviewer alignment, and product-endorser matching. Most of existing methods for this problem usually find “relevant” experts for each query independently by using, e.g., an information retrieval method. However, in real-world systems, various domain-specific constraints must be considered. For example, to review a paper, it is desirable that there is at least one senior reviewer to guide the reviewing process. An important question is: “Can we design a framework to efficiently find the optimal solution for expertise matching under various constraints?” This paper explores such an approach by formulating the expertise matching problem in a constraint based optimization framework. Interestingly, the problem can be linked to a convex cost flow problem, which guarantees an optimal solution under given constraints. We also present an online matching algorithm to support incorporating user feedbacks in real time. The proposed approach has been evaluated on two different genres of expertise matching problems. Experimental results validate the effectiveness of the proposed approach.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125626797","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}
Representing web data into a machine understandable format is a curtail task for the next generation of the web. Most of current web pages are dynamic pages. A large percentage of these web pages get their contents from underlying database. This work proposes an approach to represent dynamic web pages into Concept Description Language (CDL) semantic format. This format does not depend on ontologies which are domain dependant. However, CDL describes semantic structure of web content based on a set of semantic relations.
{"title":"Semantic Structure Content for Dynamic Web Pages","authors":"M. Farouk, M. Ishizuka","doi":"10.1109/WI-IAT.2010.290","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.290","url":null,"abstract":"Representing web data into a machine understandable format is a curtail task for the next generation of the web. Most of current web pages are dynamic pages. A large percentage of these web pages get their contents from underlying database. This work proposes an approach to represent dynamic web pages into Concept Description Language (CDL) semantic format. This format does not depend on ontologies which are domain dependant. However, CDL describes semantic structure of web content based on a set of semantic relations.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"31 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113967926","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}
Representing meaning is a major challenge facing Web 3.0. However, it is extremely difficult to excavate the meaning of a target concept from textual data as there is no one-to-one correspondence between the textual unit in which the target concept is embedded and the conceptual content that we would like to excavate. In this paper, we propose one possible approach for addressing this challenge, by harvesting the Web for metaphorical relations in which the target concept is an argument. In this paper, we present several preliminary results of "Pedesis" – a novel automated system for excavating the meaning of target concepts – and plan to present at the oral presentation an application of this methodology for identifying depression in free text.
{"title":"Using Web-Intelligence for Excavating the Emerging Meaning of Target-Concepts","authors":"Yair Neuman, Gabi Kedma, Yohai Cohen, Ophir Nave","doi":"10.1109/WI-IAT.2010.38","DOIUrl":"https://doi.org/10.1109/WI-IAT.2010.38","url":null,"abstract":"Representing meaning is a major challenge facing Web 3.0. However, it is extremely difficult to excavate the meaning of a target concept from textual data as there is no one-to-one correspondence between the textual unit in which the target concept is embedded and the conceptual content that we would like to excavate. In this paper, we propose one possible approach for addressing this challenge, by harvesting the Web for metaphorical relations in which the target concept is an argument. In this paper, we present several preliminary results of \"Pedesis\" – a novel automated system for excavating the meaning of target concepts – and plan to present at the oral presentation an application of this methodology for identifying depression in free text.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122626783","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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