Most of the existing recommender systems for tourism apply knowledge-based and content-based approaches, which need sufficient historical rating information or extra knowledge and suffer from the cold start problem. In this paper, a demographic recommender system is utilized for the recommendation of attractions. This system categorizes the tourists using their demographic information and then makes recommendations based on demographic classes. Its advantage is that the history of ratings and extra knowledge are not needed, so a new tourist can obtain recommendation. Focusing on the attractions on Trip Advisor, we use different machine learning methods to produce prediction of ratings, so as to determine whether these approaches and demographic information of tourists are suitable for providing recommendations. Our preliminary results show that the methods and demographic information can be used to predict tourists' ratings on attractions. But using demographic information alone can only achieve limited accuracy. More information such as textual reviews is required to improve the accuracy of the recommendation.
{"title":"Applicability of Demographic Recommender System to Tourist Attractions: A Case Study on Trip Advisor","authors":"Yuanyuan Wang, S. Chan, G. Ngai","doi":"10.1109/WI-IAT.2012.133","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.133","url":null,"abstract":"Most of the existing recommender systems for tourism apply knowledge-based and content-based approaches, which need sufficient historical rating information or extra knowledge and suffer from the cold start problem. In this paper, a demographic recommender system is utilized for the recommendation of attractions. This system categorizes the tourists using their demographic information and then makes recommendations based on demographic classes. Its advantage is that the history of ratings and extra knowledge are not needed, so a new tourist can obtain recommendation. Focusing on the attractions on Trip Advisor, we use different machine learning methods to produce prediction of ratings, so as to determine whether these approaches and demographic information of tourists are suitable for providing recommendations. Our preliminary results show that the methods and demographic information can be used to predict tourists' ratings on attractions. But using demographic information alone can only achieve limited accuracy. More information such as textual reviews is required to improve the accuracy of the recommendation.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"28 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":"115146114","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 recent years, such SNS services as Facebook, Google+, and Twitter have become very popular. In such services, many sources of information are posted and shared, although user rankings are hardly considered. In this paper, we consider for web pages an evaluation technique, such as HITS and PageRank, for SNS user evaluation applications and propose an algorithm using a user's real distance. We consider various parameters, including user distance, favorites, and the numbers of friends in SNSs in our evaluation technique. We propose a new reputation network to measure the reliability of SNS information.
{"title":"Evaluation of the Reputation Network Using Realistic Distance between Facebook Data","authors":"T. Otsuka, T. Yoshimura, Takayuki Ito","doi":"10.1109/WI-IAT.2012.85","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.85","url":null,"abstract":"In recent years, such SNS services as Facebook, Google+, and Twitter have become very popular. In such services, many sources of information are posted and shared, although user rankings are hardly considered. In this paper, we consider for web pages an evaluation technique, such as HITS and PageRank, for SNS user evaluation applications and propose an algorithm using a user's real distance. We consider various parameters, including user distance, favorites, and the numbers of friends in SNSs in our evaluation technique. We propose a new reputation network to measure the reliability of SNS information.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"72 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":"123043489","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}
Micro-blog's handiness is besieging users with overloaded short snippets of tweets surging into their page. How to evaluate quality of tweets with limited content and rank them to direct user attention is a new significant topic. In this paper, we study the problem of user-specific tweet evaluation and ranking. We propose a comprehensive, personalized tweet ranking mechanism (Tweet Rank) with a technique of AHP (Analytic Hierarchy Process) in operational research. Based on mathematics and psychology, the AHP can quantify the weight of each impact factor and model user blur preference precisely. Case study in Chinese micro-blog platform of T.sina showed that Tweet Rank greatly outperformed time-based ranking used in T.Sina, improving percentage of interesting content in Top10 to 60% from 20%.
{"title":"Personalized Tweet Ranking Based on AHP: A Case Study of Micro-blogging Message Ranking in T.Sina","authors":"Yuhong Guo, Li-Fang Kang, Tie Shi","doi":"10.1109/WI-IAT.2012.38","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.38","url":null,"abstract":"Micro-blog's handiness is besieging users with overloaded short snippets of tweets surging into their page. How to evaluate quality of tweets with limited content and rank them to direct user attention is a new significant topic. In this paper, we study the problem of user-specific tweet evaluation and ranking. We propose a comprehensive, personalized tweet ranking mechanism (Tweet Rank) with a technique of AHP (Analytic Hierarchy Process) in operational research. Based on mathematics and psychology, the AHP can quantify the weight of each impact factor and model user blur preference precisely. Case study in Chinese micro-blog platform of T.sina showed that Tweet Rank greatly outperformed time-based ranking used in T.Sina, improving percentage of interesting content in Top10 to 60% from 20%.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"41 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":"117152427","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 multi-agent task scheduling one tries to find a joint schedule for a set of time-constrained tasks, where each agent is responsible for scheduling a disjoint subset of tasks. Two important problems occurring here are (i) to find a joint schedule providing maximum flexibility, i.e., a schedule that maximizes the freedom agents have in choosing the exact time they would like to start their tasks without violating scheduling constraints, (ii) to find an optimal decoupling of the original problem such that each of the agents is able to solve its own part of the task scheduling problem independently of the other agents and with maximum total flexibility. In this paper we show that both problems are closely related. We use a running example derived from a real maintenance scheduling problem occurring at Ned Train, the national Dutch railway maintenance company.
{"title":"Maximum Flexibility and Optimal Decoupling in Task Scheduling Problems","authors":"Leon Endhoven, T. Klos, C. Witteveen","doi":"10.1109/WI-IAT.2012.149","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.149","url":null,"abstract":"In multi-agent task scheduling one tries to find a joint schedule for a set of time-constrained tasks, where each agent is responsible for scheduling a disjoint subset of tasks. Two important problems occurring here are (i) to find a joint schedule providing maximum flexibility, i.e., a schedule that maximizes the freedom agents have in choosing the exact time they would like to start their tasks without violating scheduling constraints, (ii) to find an optimal decoupling of the original problem such that each of the agents is able to solve its own part of the task scheduling problem independently of the other agents and with maximum total flexibility. In this paper we show that both problems are closely related. We use a running example derived from a real maintenance scheduling problem occurring at Ned Train, the national Dutch railway maintenance company.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"20 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":"120980502","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 a Markov Clustering (MCL) based text mining approach for namesake disambiguation on the Web. The novelty of the proposed technique lies in modeling the collection of web pages using a weighted graph structure and applying MCL to crystalize it into different clusters, each one containing the web pages related to a particular namesake individual. The proposed method focuses on three broad and realistic aspects to cluster web pages retrieved through search engines - content overlapping, structure overlapping, and local context overlapping. The efficacy of the proposed method is demonstrated through experimental evaluations on standard datasets.
{"title":"An MCL-Based Text Mining Approach for Namesake Disambiguation on the Web","authors":"Tarique Anwar, M. Abulaish","doi":"10.1109/WI-IAT.2012.239","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.239","url":null,"abstract":"In this paper, we propose a Markov Clustering (MCL) based text mining approach for namesake disambiguation on the Web. The novelty of the proposed technique lies in modeling the collection of web pages using a weighted graph structure and applying MCL to crystalize it into different clusters, each one containing the web pages related to a particular namesake individual. The proposed method focuses on three broad and realistic aspects to cluster web pages retrieved through search engines - content overlapping, structure overlapping, and local context overlapping. The efficacy of the proposed method is demonstrated through experimental evaluations on standard datasets.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"74 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":"127355409","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}
Bo Li, Jian-wei Gong, Yan Jiang, Hany Nasry, Guang-ming Xiong
A* path planning algorithm cannot always guarantee the continuity of a robot's movements when the allocated time is limited, however Anytime Repairing A*(ARA*) can get a sub-optimal solution quickly, and then work on improving the solution until the allocated time expires. This paper proposes a variation of ARA* algorithm (ARA*+) which executes multiple Weighted A* to search the solution. During the first search of ARA*+, Weighted A* with a bigger inflation factor is applied and no state is expanded more than once, in this way, the time needed for finding a sub-optimal solution can be remarkably shortened. Then, Weighted A* will be executed again for better path, by decreasing the inflation factor and reusing the previous planning efforts. Here, with the same inflation factor the expanded states can be used again, and this is different from ARA*, which forbids the expanded states to be expanded again. If the allocated time does not expire, this process will not stop until the optimal solution is found, or the current sub-optimal solution will be regarded as the output. According to our robot path planning experiments, in most cases the number of expanded states in ARA*+ was smaller than that in ARA*, as a result, the time spent to get the optimal solution will be shorter.
{"title":"ARA*+: Improved Path Planning Algorithm Based on ARA*","authors":"Bo Li, Jian-wei Gong, Yan Jiang, Hany Nasry, Guang-ming Xiong","doi":"10.1109/WI-IAT.2012.13","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.13","url":null,"abstract":"A* path planning algorithm cannot always guarantee the continuity of a robot's movements when the allocated time is limited, however Anytime Repairing A*(ARA*) can get a sub-optimal solution quickly, and then work on improving the solution until the allocated time expires. This paper proposes a variation of ARA* algorithm (ARA*+) which executes multiple Weighted A* to search the solution. During the first search of ARA*+, Weighted A* with a bigger inflation factor is applied and no state is expanded more than once, in this way, the time needed for finding a sub-optimal solution can be remarkably shortened. Then, Weighted A* will be executed again for better path, by decreasing the inflation factor and reusing the previous planning efforts. Here, with the same inflation factor the expanded states can be used again, and this is different from ARA*, which forbids the expanded states to be expanded again. If the allocated time does not expire, this process will not stop until the optimal solution is found, or the current sub-optimal solution will be regarded as the output. According to our robot path planning experiments, in most cases the number of expanded states in ARA*+ was smaller than that in ARA*, as a result, the time spent to get the optimal solution will be shorter.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"45 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":"124824574","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}
Past research on multi-agent simulation with cooperative reinforcement learning (RL) focuses on developing sharing strategies that are adopted and used by all agents in the environment. In this paper, we target situations where this assumption of a single sharing strategy that is employed by all agents is not valid. We seek to address how agents with no predetermined sharing partners can exploit groups of cooperatively learning agents to improve learning performance when compared to Independent learning. Specifically, we propose 3 intra-agent methods that do not assume a reciprocating sharing relationship and leverage the pre-existing agent interface associated with Q-Learning to expedite learning.
{"title":"Non-reciprocating Sharing Methods in Cooperative Q-Learning Environments","authors":"B. Cunningham, Yong Cao","doi":"10.1109/WI-IAT.2012.28","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.28","url":null,"abstract":"Past research on multi-agent simulation with cooperative reinforcement learning (RL) focuses on developing sharing strategies that are adopted and used by all agents in the environment. In this paper, we target situations where this assumption of a single sharing strategy that is employed by all agents is not valid. We seek to address how agents with no predetermined sharing partners can exploit groups of cooperatively learning agents to improve learning performance when compared to Independent learning. Specifically, we propose 3 intra-agent methods that do not assume a reciprocating sharing relationship and leverage the pre-existing agent interface associated with Q-Learning to expedite learning.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"67 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":"124950093","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}
Automatically computing the semantic relatedness of two words is an essential step for many tasks in natural language processing, including information retrieval. Previous approaches to computing semantic relatedness used statistical techniques or lexical resources. We propose Searcher Result Analysis (SRA), a novel method that captures related text from search engine by issuing proper queries. Inferring the relatedness is then based on word occurrences in certain number of pages. Compared with the previous state of the art, using SRA to computing semantic relatedness based on Wikipedia can achieve competitive results with no need to maintain a local copy of remote resources. It is also shown that the correctness can be further improved by selecting proper knowledge resources or corpora for SRA.
{"title":"Computing Semantic Relatedness Based on Search Result Analysis","authors":"Jiangjiao Duan, Jianping Zeng","doi":"10.1109/WI-IAT.2012.29","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.29","url":null,"abstract":"Automatically computing the semantic relatedness of two words is an essential step for many tasks in natural language processing, including information retrieval. Previous approaches to computing semantic relatedness used statistical techniques or lexical resources. We propose Searcher Result Analysis (SRA), a novel method that captures related text from search engine by issuing proper queries. Inferring the relatedness is then based on word occurrences in certain number of pages. Compared with the previous state of the art, using SRA to computing semantic relatedness based on Wikipedia can achieve competitive results with no need to maintain a local copy of remote resources. It is also shown that the correctness can be further improved by selecting proper knowledge resources or corpora for SRA.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"10 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":"125013889","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}
Nowadays, people receive information of the news stories not only from newspapers but also from online news websites. They search important news stories in order to know what happen today. However, it is hard to browse all the news stories published on a day. It is necessary to identify which news stories are more newsworthy on the specific day. In this paper, we investigate how to automatically identify the importance of news stories for different news categories on a specific day by utilizing the influence propagation among communities and news categories. In particular, we build an influence propagation model which consists of three features: category relevance, bloggers' attention and bursty influence. Based on this influence propagation model, we propose a Cross-Category Social Influence Propagation (C-SIP) approach for scoring the importance of news stories on a specific day. We evaluate our approach by using the judgment of Story Ranking Task in TREC 2010 Blog Track. The experiment shows our approach attains a prominent performance in the retrieval of important news stories and gets 9.94% improvement over the best performance of participating systems in TREC 2010 Blog Track.
如今，人们不仅从报纸上获得新闻报道的信息，还从在线新闻网站上获得新闻报道的信息。他们搜索重要的新闻故事，以便了解今天发生了什么。然而，很难浏览一天发布的所有新闻故事。有必要确定哪些新闻报道在特定的一天更有新闻价值。在本文中，我们研究了如何利用社区和新闻类别之间的影响力传播来自动识别特定日期不同新闻类别的新闻故事的重要性。特别地，我们建立了一个包含类别相关性、博主关注度和突发影响力三个特征的影响力传播模型。基于这种影响传播模型，我们提出了一种跨类别社会影响传播(C-SIP)方法来对特定日期的新闻故事的重要性进行评分。我们使用TREC 2010 Blog Track中的故事排序任务来评估我们的方法。实验表明，我们的方法在重要新闻故事的检索中取得了突出的性能，比TREC 2010 Blog Track中参与系统的最佳性能提高了9.94%。
{"title":"The Retrieval of Important News Stories by Influence Propagation among Communities and Categories","authors":"Yu-Fan Lin, Hung-Yu kao","doi":"10.1109/WI-IAT.2012.236","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.236","url":null,"abstract":"Nowadays, people receive information of the news stories not only from newspapers but also from online news websites. They search important news stories in order to know what happen today. However, it is hard to browse all the news stories published on a day. It is necessary to identify which news stories are more newsworthy on the specific day. In this paper, we investigate how to automatically identify the importance of news stories for different news categories on a specific day by utilizing the influence propagation among communities and news categories. In particular, we build an influence propagation model which consists of three features: category relevance, bloggers' attention and bursty influence. Based on this influence propagation model, we propose a Cross-Category Social Influence Propagation (C-SIP) approach for scoring the importance of news stories on a specific day. We evaluate our approach by using the judgment of Story Ranking Task in TREC 2010 Blog Track. The experiment shows our approach attains a prominent performance in the retrieval of important news stories and gets 9.94% improvement over the best performance of participating systems in TREC 2010 Blog Track.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"23 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":"125591467","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}
Graph vertices are often divided into groups or communities with dense connections within communities and sparse connections between communities. Community detection has recently attracted considerable attention in the field of data mining and social network analysis. Existing community detection methods require too much space and are very time consuming for moderate-to-large networks, whereas large-scale networks have become ubiquitous in real world. We proposed a method that can find communities of a graph with good time and space complexity and good accuracy as well.
{"title":"A Modularity Maximization Algorithm for Community Detection in Social Networks with Low Time Complexity","authors":"Mohsen Arab, M. Afsharchi","doi":"10.1109/WI-IAT.2012.97","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.97","url":null,"abstract":"Graph vertices are often divided into groups or communities with dense connections within communities and sparse connections between communities. Community detection has recently attracted considerable attention in the field of data mining and social network analysis. Existing community detection methods require too much space and are very time consuming for moderate-to-large networks, whereas large-scale networks have become ubiquitous in real world. We proposed a method that can find communities of a graph with good time and space complexity and good accuracy as well.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"162 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":"116163097","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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