Customers of a webshop are often presented large assortments, which can lead to customers struggling finding their desired product(s), an issue known as choice overload. In order to overcome this issue, recommender systems are used in webshops to provide personalized product recommendations to customers. Though, recommender systems using matrix factorization are not able to provide recommendations to new customers (i.e., cold users). To facilitate recommendations to cold users we investigate multiple active learning strategies, and subsequently evaluate which active learning strategy is able to optimally elicit the preferences from the cold users. Our model is empirically validated using a dataset from the webshop of de Bijenkorf, a Dutch department store. We find that the overall best-performing active learning strategy is PopGini, an active learning strategy which combines the popularity of an item with its Gini impurity score.
{"title":"Addressing the cold user problem for model-based recommender systems","authors":"Tomas Geurts, F. Frasincar","doi":"10.1145/3106426.3106431","DOIUrl":"https://doi.org/10.1145/3106426.3106431","url":null,"abstract":"Customers of a webshop are often presented large assortments, which can lead to customers struggling finding their desired product(s), an issue known as choice overload. In order to overcome this issue, recommender systems are used in webshops to provide personalized product recommendations to customers. Though, recommender systems using matrix factorization are not able to provide recommendations to new customers (i.e., cold users). To facilitate recommendations to cold users we investigate multiple active learning strategies, and subsequently evaluate which active learning strategy is able to optimally elicit the preferences from the cold users. Our model is empirically validated using a dataset from the webshop of de Bijenkorf, a Dutch department store. We find that the overall best-performing active learning strategy is PopGini, an active learning strategy which combines the popularity of an item with its Gini impurity score.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77545667","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 proposed a novel scheme for extracting character relation from unstructured text based on dependency grammar rules. First of all, we took the Three Kingdoms characters as our research object, then selected articles containing target relationships and thus constructed a corpus consisting of 1000 sentences. Secondly, We analyzed the corpus and developed a set of dependent grammar rules for relation extraction. Finally, we proposed a system, which makes it possible for computers to automatically extract and identify character relationships.
{"title":"Ontology of human relation extraction based on dependency syntax rules","authors":"Long He, Likun Qiu","doi":"10.1145/3106426.3109050","DOIUrl":"https://doi.org/10.1145/3106426.3109050","url":null,"abstract":"This paper proposed a novel scheme for extracting character relation from unstructured text based on dependency grammar rules. First of all, we took the Three Kingdoms characters as our research object, then selected articles containing target relationships and thus constructed a corpus consisting of 1000 sentences. Secondly, We analyzed the corpus and developed a set of dependent grammar rules for relation extraction. Finally, we proposed a system, which makes it possible for computers to automatically extract and identify character relationships.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76215119","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}
Ordinal regression has received increasing interest in the past years. It aims to classify patterns by an ordinal scale. With the the explosive growth of data, the method of SVM with ordinal partitioning called SVMOP highlights its advantages due to its convenience of dealing with large scale data. However, the method of SVMOP for ordinal regression has not been exploited much. As we know, the costs should be different when dealing with mislabeled samples and how to use them plays a dominant role in model building. However, L2-loss which could enlarge the cost sensitivity has not been applied into SVM ordinal partition yet. In this paper, we propose the method of SVMOP with L2-loss for ordinal regression. Numerical results show that our approach outperforms the method of SVMOP with L1-loss and other ordianl regression models.
{"title":"Augmented SVM with ordinal partitioning for text classification","authors":"Yong Shi, Peijia Li, Lingfeng Niu","doi":"10.1145/3106426.3109428","DOIUrl":"https://doi.org/10.1145/3106426.3109428","url":null,"abstract":"Ordinal regression has received increasing interest in the past years. It aims to classify patterns by an ordinal scale. With the the explosive growth of data, the method of SVM with ordinal partitioning called SVMOP highlights its advantages due to its convenience of dealing with large scale data. However, the method of SVMOP for ordinal regression has not been exploited much. As we know, the costs should be different when dealing with mislabeled samples and how to use them plays a dominant role in model building. However, L2-loss which could enlarge the cost sensitivity has not been applied into SVM ordinal partition yet. In this paper, we propose the method of SVMOP with L2-loss for ordinal regression. Numerical results show that our approach outperforms the method of SVMOP with L1-loss and other ordianl regression models.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74502104","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}
A great deal of such texts as news and blog articles, web pages, and scientific literature are posted on the web as time goes by, and are generally called time-series documents or text streams. For each document, some strongly or weakly relevant texts exist. Although such relevance is represented as citations among scientific literatures, trackback among blog articles, hyperlinks among Wikipedia articles or web pages and so on, the relevance among news articles is not always clearly specified. One easy way to build a similarity network is by calculating the similarity among news articles and making links among similar articles; however, adding information about the posted times of articles to a similarity network is difficult. To overcome this problem, we propose a framework that consists of two parts: 1) tree structures called Topic Forests and 2) their visualization. Topic Forests are constructed by semantically and temporally linking cohesive texts while preserving their posted order. We provide effective access for users to text streams by embedding Topic Forests over the polar coordinates with a technique called Polar Coordinate Embedding. From experimental evaluations using the actual text streams of news articles, we confirm that Topic Forests semantically and temporally maintain cohesiveness, and Polar Coordinate Embedding achieves effective accessibility.
{"title":"Constructing and visualizing topic forests for text streams","authors":"Takayasu Fushimi, T. Satoh","doi":"10.1145/3106426.3106455","DOIUrl":"https://doi.org/10.1145/3106426.3106455","url":null,"abstract":"A great deal of such texts as news and blog articles, web pages, and scientific literature are posted on the web as time goes by, and are generally called time-series documents or text streams. For each document, some strongly or weakly relevant texts exist. Although such relevance is represented as citations among scientific literatures, trackback among blog articles, hyperlinks among Wikipedia articles or web pages and so on, the relevance among news articles is not always clearly specified. One easy way to build a similarity network is by calculating the similarity among news articles and making links among similar articles; however, adding information about the posted times of articles to a similarity network is difficult. To overcome this problem, we propose a framework that consists of two parts: 1) tree structures called Topic Forests and 2) their visualization. Topic Forests are constructed by semantically and temporally linking cohesive texts while preserving their posted order. We provide effective access for users to text streams by embedding Topic Forests over the polar coordinates with a technique called Polar Coordinate Embedding. From experimental evaluations using the actual text streams of news articles, we confirm that Topic Forests semantically and temporally maintain cohesiveness, and Polar Coordinate Embedding achieves effective accessibility.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84991192","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}
For distance learning applications, inferring the cognitive states of students, particularly, their concentration and comprehension levels during instruction, is important to assess their learning efficacy. In this paper, we investigated the feasibility of using EEG recordings generated from an off-the-shelf, wearable device to automatically classify the cognitive states of students as they were asked to perform a series of reading and question answering tasks. We showed that the EEG data can effectively predict whether a student is attentive or distracted as well as the student's reading speed, which is an important measure of reading fluency. However, the EEG signals alone are insufficient to predict how well the students can correctly answer questions related to the reading materials as there were other confounding factors, such as the students' background knowledge, that must be taken into consideration. We also showed that the accuracy in predicting the different cognitive states depends on the choice of classifier used (global, local, or multi-task learning). For example, the concentration level of a student can be accurately predicted using a local model whereas a global model that incorporates side information about the student's background knowledge is more effective at predicting whether the student will correctly answer questions about the materials they read.
{"title":"Automated classification of EEG signals for predicting students' cognitive state during learning","authors":"Xi Liu, P. Tan, Lei Liu, S. Simske","doi":"10.1145/3106426.3106453","DOIUrl":"https://doi.org/10.1145/3106426.3106453","url":null,"abstract":"For distance learning applications, inferring the cognitive states of students, particularly, their concentration and comprehension levels during instruction, is important to assess their learning efficacy. In this paper, we investigated the feasibility of using EEG recordings generated from an off-the-shelf, wearable device to automatically classify the cognitive states of students as they were asked to perform a series of reading and question answering tasks. We showed that the EEG data can effectively predict whether a student is attentive or distracted as well as the student's reading speed, which is an important measure of reading fluency. However, the EEG signals alone are insufficient to predict how well the students can correctly answer questions related to the reading materials as there were other confounding factors, such as the students' background knowledge, that must be taken into consideration. We also showed that the accuracy in predicting the different cognitive states depends on the choice of classifier used (global, local, or multi-task learning). For example, the concentration level of a student can be accurately predicted using a local model whereas a global model that incorporates side information about the student's background knowledge is more effective at predicting whether the student will correctly answer questions about the materials they read.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84865331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
News articles from different sources regularly report crime incidents that contain details of crime, information about accused entities, details of the investigation process and finally details of judgement. In this paper, we have proposed natural language processing techniques for extraction and curation of crime-related information from digitally published News articles. We have leveraged computational linguistics based methods to analyse crime related News documents to extract different crime related entities and events. This includes name of the criminal, name of the victim, nature of crime, geographic location, date and time, and action taken against the criminal. We have also proposed a semi-supervised learning technique to learn different categories of crime events from the News documents. This helps in continuous evolution of the crime dictionaries. Thus the proposed methods are not restricted to detecting known crimes only but contribute actively towards maintaining an updated crime dictionary. We have done experiments with a collection of 3000 crime-reporting News articles. The end-product of our experiments is a crime-register that contains details of crime committed across geographies and time. This register can be further utilized for analytical and reporting purposes.
{"title":"CrimeProfiler: crime information extraction and visualization from news media","authors":"Tirthankar Dasgupta, Abir Naskar, Rupsa Saha, Lipika Dey","doi":"10.1145/3106426.3106476","DOIUrl":"https://doi.org/10.1145/3106426.3106476","url":null,"abstract":"News articles from different sources regularly report crime incidents that contain details of crime, information about accused entities, details of the investigation process and finally details of judgement. In this paper, we have proposed natural language processing techniques for extraction and curation of crime-related information from digitally published News articles. We have leveraged computational linguistics based methods to analyse crime related News documents to extract different crime related entities and events. This includes name of the criminal, name of the victim, nature of crime, geographic location, date and time, and action taken against the criminal. We have also proposed a semi-supervised learning technique to learn different categories of crime events from the News documents. This helps in continuous evolution of the crime dictionaries. Thus the proposed methods are not restricted to detecting known crimes only but contribute actively towards maintaining an updated crime dictionary. We have done experiments with a collection of 3000 crime-reporting News articles. The end-product of our experiments is a crime-register that contains details of crime committed across geographies and time. This register can be further utilized for analytical and reporting purposes.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73510322","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}
Online social networks are facing serious threats due to presence of human-behaviour imitating malicious bots (aka socialbots) that are successful mainly due to existence of their duped followers. In this paper, we propose an approach to categorize Twitter users into three groups - active, reactive, and inactive targets, based on their interaction behaviour with socialbots. Active users are those who themselves follow socialbots without being followed by them, reactive users respond to the following socialbots by following them back, whereas inactive users do not show any interest against the following requests from anonymous socialbots. The proposed approach is modelled as both binary and ternary classification problem, wherein users' profile is generated using static and dynamic components representing their identical and behavioural aspects. Three different classification techniques viz Naive Bayes, Reduced Error Pruned Decision Tree, and Random Forest are used over a dataset of 749 users collected through live experiment, and a thorough analyses of the identified users categories is presented, wherein it is found that active and reactive users keep on frequently updating their tweets containing advertising related contents. Finally, feature ranking algorithms are used to rank identified features to analyse their discriminative power, and it is found that following rate and follower rate are the most dominating features.
{"title":"Identifying active, reactive, and inactive targets of socialbots in Twitter","authors":"Mohd Fazil, M. Abulaish","doi":"10.1145/3106426.3106483","DOIUrl":"https://doi.org/10.1145/3106426.3106483","url":null,"abstract":"Online social networks are facing serious threats due to presence of human-behaviour imitating malicious bots (aka socialbots) that are successful mainly due to existence of their duped followers. In this paper, we propose an approach to categorize Twitter users into three groups - active, reactive, and inactive targets, based on their interaction behaviour with socialbots. Active users are those who themselves follow socialbots without being followed by them, reactive users respond to the following socialbots by following them back, whereas inactive users do not show any interest against the following requests from anonymous socialbots. The proposed approach is modelled as both binary and ternary classification problem, wherein users' profile is generated using static and dynamic components representing their identical and behavioural aspects. Three different classification techniques viz Naive Bayes, Reduced Error Pruned Decision Tree, and Random Forest are used over a dataset of 749 users collected through live experiment, and a thorough analyses of the identified users categories is presented, wherein it is found that active and reactive users keep on frequently updating their tweets containing advertising related contents. Finally, feature ranking algorithms are used to rank identified features to analyse their discriminative power, and it is found that following rate and follower rate are the most dominating features.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74951068","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}
Shokirkhon Oppokhonov, Seyoung Park, Isaac. K. E. Ampomah
Availability of large volume of community contributed location data enables a lot of location providing services and these services have attracted many industries and academic researchers by its importance. In this paper we propose the new recommender system that recommends the new POI for next hours. First we find the users with similar check-in sequences and depict their check-in sequences as a directed graph, then find the users current location. To recommend the new POI recommendation for next hour we refer to the directed graph we have created. Our algorithm considers both the temporal factor i.e., recommendation time, and the spatial(distance) at the same time. We conduct an experiment on random data collected from Foursquare and Gowalla. Experiment results show that our proposed model outperforms the collaborative-filtering based state-of-the-art recommender techniques.
{"title":"Current location-based next POI recommendation","authors":"Shokirkhon Oppokhonov, Seyoung Park, Isaac. K. E. Ampomah","doi":"10.1145/3106426.3106528","DOIUrl":"https://doi.org/10.1145/3106426.3106528","url":null,"abstract":"Availability of large volume of community contributed location data enables a lot of location providing services and these services have attracted many industries and academic researchers by its importance. In this paper we propose the new recommender system that recommends the new POI for next hours. First we find the users with similar check-in sequences and depict their check-in sequences as a directed graph, then find the users current location. To recommend the new POI recommendation for next hour we refer to the directed graph we have created. Our algorithm considers both the temporal factor i.e., recommendation time, and the spatial(distance) at the same time. We conduct an experiment on random data collected from Foursquare and Gowalla. Experiment results show that our proposed model outperforms the collaborative-filtering based state-of-the-art recommender techniques.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77535947","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 abundance of reviews published on the Web about a given product, consumers are looking for ways to view major opinions that can be presented in a quick and succinct way. Reviews contain many different opinions, making the ability to show a diversified review summary that focus on coverage and diversity a major goal. Most review summarization work focuses on showing salient reviews as a summary which might ignore diversity in summaries. In this paper, we present a graph-based algorithm that is capable of producing extractive summaries that are both diversified from a sentiment point of view and topically well-covered. First, we use statistical measures to find topical words. Then we split the dataset based on the sentiment class of the reviews and perform the ranking on each sentiment graph. When compared with different baselines, our approach scores best in most ROUGE metrics. Specifically, our approach shows improvements of 3.9% in ROUGE-1 and 1.8% in ROUGE-L in comparison with the best competing baseline.
{"title":"Sentiment diversification for short review summarization","authors":"Mohammed Al-Dhelaan, A. Al-Suhaim","doi":"10.1145/3106426.3106525","DOIUrl":"https://doi.org/10.1145/3106426.3106525","url":null,"abstract":"With the abundance of reviews published on the Web about a given product, consumers are looking for ways to view major opinions that can be presented in a quick and succinct way. Reviews contain many different opinions, making the ability to show a diversified review summary that focus on coverage and diversity a major goal. Most review summarization work focuses on showing salient reviews as a summary which might ignore diversity in summaries. In this paper, we present a graph-based algorithm that is capable of producing extractive summaries that are both diversified from a sentiment point of view and topically well-covered. First, we use statistical measures to find topical words. Then we split the dataset based on the sentiment class of the reviews and perform the ranking on each sentiment graph. When compared with different baselines, our approach scores best in most ROUGE metrics. Specifically, our approach shows improvements of 3.9% in ROUGE-1 and 1.8% in ROUGE-L in comparison with the best competing baseline.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81614606","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 agent-based systems, an agent generally forms her belief based on evidence from multiple sources, such as messages from other agents or perception of the external environment. In this paper, we present a logic for reasoning about evidence and belief. Our framework not only takes advantage of the source-tracking capability of justification logic, but also allows the distinction between the actual observation and simply potential admissibility of evidence. We present the axiomatization for the basic logic and its dynamic extension, investigate its properties, and use a running example to show its applicability to information fusion for autonomous agents.
{"title":"A logic for reasoning about evidence and belief","authors":"T. Fan, C. Liau","doi":"10.1145/3106426.3106519","DOIUrl":"https://doi.org/10.1145/3106426.3106519","url":null,"abstract":"In agent-based systems, an agent generally forms her belief based on evidence from multiple sources, such as messages from other agents or perception of the external environment. In this paper, we present a logic for reasoning about evidence and belief. Our framework not only takes advantage of the source-tracking capability of justification logic, but also allows the distinction between the actual observation and simply potential admissibility of evidence. We present the axiomatization for the basic logic and its dynamic extension, investigate its properties, and use a running example to show its applicability to information fusion for autonomous agents.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83129688","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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