Improving user experience through personalized recommendations is crucial to organizing the abundance of data on news websites. Modeling user preferences based on implicit feedback has recently gained lots of attention, partly due to growing volume of web generated click stream data. Matrix factorization learned with stochastic gradient descent has successfully been adopted to approximate various ranking objectives. The aim of this paper is to test the performance of learning to rank approaches on the real-world dataset and apply some simple heuristics to consider temporal dynamics present in news domain. Our model is based on WARP loss with changes to classic factorization model.
{"title":"Learning to rank for personalized news recommendation","authors":"Pavel Shashkin, N. Karpov","doi":"10.1145/3106426.3109432","DOIUrl":"https://doi.org/10.1145/3106426.3109432","url":null,"abstract":"Improving user experience through personalized recommendations is crucial to organizing the abundance of data on news websites. Modeling user preferences based on implicit feedback has recently gained lots of attention, partly due to growing volume of web generated click stream data. Matrix factorization learned with stochastic gradient descent has successfully been adopted to approximate various ranking objectives. The aim of this paper is to test the performance of learning to rank approaches on the real-world dataset and apply some simple heuristics to consider temporal dynamics present in news domain. Our model is based on WARP loss with changes to classic factorization model.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77606566","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}
Jingjing Tang, Ying-jie Tian, Guoqiang Wu, Dewei Li
In recent years, nonparallel support vector machine (NPSVM) is proposed as a nonparallel hyperplane classifier with superior performance than standard SVM and existing nonparallel classifiers such as the twin support vector machine (TWSVM). With the perfect theoretical underpinnings and great practical success, NPSVM has been used to dealing with the classification tasks on different scales. Tackling large-scale classification problem is a challenge yet significant work. Although large-scale linear NPSVM model has already been efficiently solved by the dual coordinate descent (DCD) algorithm or alternating direction method of multipliers (ADMM), we present a new strategy to solve the primal form of linear NPSVM different from existing work in this paper. Our algorithm is designed in the framework of the stochastic gradient descent (SGD), which is well suited to large-scale problem. Experiments are conducted on five large-scale data sets to confirm the effectiveness of our method.
{"title":"Stochastic gradient descent for large-scale linear nonparallel SVM","authors":"Jingjing Tang, Ying-jie Tian, Guoqiang Wu, Dewei Li","doi":"10.1145/3106426.3109427","DOIUrl":"https://doi.org/10.1145/3106426.3109427","url":null,"abstract":"In recent years, nonparallel support vector machine (NPSVM) is proposed as a nonparallel hyperplane classifier with superior performance than standard SVM and existing nonparallel classifiers such as the twin support vector machine (TWSVM). With the perfect theoretical underpinnings and great practical success, NPSVM has been used to dealing with the classification tasks on different scales. Tackling large-scale classification problem is a challenge yet significant work. Although large-scale linear NPSVM model has already been efficiently solved by the dual coordinate descent (DCD) algorithm or alternating direction method of multipliers (ADMM), we present a new strategy to solve the primal form of linear NPSVM different from existing work in this paper. Our algorithm is designed in the framework of the stochastic gradient descent (SGD), which is well suited to large-scale problem. Experiments are conducted on five large-scale data sets to confirm the effectiveness of our method.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"192 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77630169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Information radiators are ubiquitous stationary installations that radiate information that is likely to improve awareness of passers-by in semi-public environments like organization floors. In this paper, we present the idea of using several kinds of information radiators for enhancing urban participation of seniors - by providing awareness for supporting the planning and execution of activities in public environments. We motivate the idea and discuss interaction design as well as HCI challenges to be addressed in future work.1
{"title":"Information radiators: using large screens and small devices to support awareness in urban space","authors":"Michael Koch, Anna Kötteritzsch, Julian Fietkau","doi":"10.1145/3106426.3109039","DOIUrl":"https://doi.org/10.1145/3106426.3109039","url":null,"abstract":"Information radiators are ubiquitous stationary installations that radiate information that is likely to improve awareness of passers-by in semi-public environments like organization floors. In this paper, we present the idea of using several kinds of information radiators for enhancing urban participation of seniors - by providing awareness for supporting the planning and execution of activities in public environments. We motivate the idea and discuss interaction design as well as HCI challenges to be addressed in future work.1","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"645 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77663745","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}
Emotions have a significant impact on the purchasing process. Due to novel affective computing approaches, affective information of users can be acquired in implicit and therefore non-intrusive manner. Recent research in the field of recommender systems indicates that the incorporation of affective user information in the prediction model has a positive impact on the recommender systems accuracy. Existing research mainly focused on product recommendations in the movie anfd music domain. Our paper investigates the impact of affective emotions on fashion products, which is one of the largest consumer industries. We integrate the users' mood and their emotion in the prediction model, and the results are compared to the baseline model using rating data only. For this, we generate a dataset with 337 participants, 64 products, and 10816 ratings. We determine the mood information using the PANAS questionnaire, and the emotion by using the SAM self-assessment method. The affective information is integrated leveraging Factorization Machines. The evaluation of the offline experiments reveals that in new item cold-start scenarios the mood information has a positive impact on the prediction accuracy, whereas the emotion information has a negative impact.
{"title":"Emotions and fashion recommendations: evaluating the predictive power of affective information for the prediction of fashion product preferences in cold-start scenarios","authors":"Alexander Piazza, Pavlina Kröckel, F. Bodendorf","doi":"10.1145/3106426.3109441","DOIUrl":"https://doi.org/10.1145/3106426.3109441","url":null,"abstract":"Emotions have a significant impact on the purchasing process. Due to novel affective computing approaches, affective information of users can be acquired in implicit and therefore non-intrusive manner. Recent research in the field of recommender systems indicates that the incorporation of affective user information in the prediction model has a positive impact on the recommender systems accuracy. Existing research mainly focused on product recommendations in the movie anfd music domain. Our paper investigates the impact of affective emotions on fashion products, which is one of the largest consumer industries. We integrate the users' mood and their emotion in the prediction model, and the results are compared to the baseline model using rating data only. For this, we generate a dataset with 337 participants, 64 products, and 10816 ratings. We determine the mood information using the PANAS questionnaire, and the emotion by using the SAM self-assessment method. The affective information is integrated leveraging Factorization Machines. The evaluation of the offline experiments reveals that in new item cold-start scenarios the mood information has a positive impact on the prediction accuracy, whereas the emotion information has a negative impact.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74356836","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}
More than 500 million facts on the Linked Data Web are statements across knowledge bases. These links are of crucial importance for the Linked Data Web as they make a large number of tasks possible, including cross-ontology, question answering and federated queries. However, a large number of these links are erroneous and can thus lead to these applications producing absurd results. We present a time-efficient and complete approach for the detection of erroneous links for properties that are transitive. To this end, we make use of the semantics of URIs on the Data Web and combine it with an efficient graph partitioning algorithm. We then apply our algorithm to the LinkLion repository and show that we can analyze 19,200,114 links in 4.6 minutes. Our results show that at least 13% of the owl :sameAs links we considered are erroneous. In addition, our analysis of the provenance of links allows discovering agents and knowledge bases that commonly display poor linking. Our algorithm can be easily executed in parallel and on a GPU. We show that these implementations are up to two orders of magnitude faster than classical reasoners and a non-parallel implementation.
{"title":"CEDAL: time-efficient detection of erroneous links in large-scale link repositories","authors":"André Valdestilhas, Tommaso Soru, A. N. Ngomo","doi":"10.1145/3106426.3106497","DOIUrl":"https://doi.org/10.1145/3106426.3106497","url":null,"abstract":"More than 500 million facts on the Linked Data Web are statements across knowledge bases. These links are of crucial importance for the Linked Data Web as they make a large number of tasks possible, including cross-ontology, question answering and federated queries. However, a large number of these links are erroneous and can thus lead to these applications producing absurd results. We present a time-efficient and complete approach for the detection of erroneous links for properties that are transitive. To this end, we make use of the semantics of URIs on the Data Web and combine it with an efficient graph partitioning algorithm. We then apply our algorithm to the LinkLion repository and show that we can analyze 19,200,114 links in 4.6 minutes. Our results show that at least 13% of the owl :sameAs links we considered are erroneous. In addition, our analysis of the provenance of links allows discovering agents and knowledge bases that commonly display poor linking. Our algorithm can be easily executed in parallel and on a GPU. We show that these implementations are up to two orders of magnitude faster than classical reasoners and a non-parallel implementation.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76784114","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}
D. Martins, G. Vossen, Fernando Buarque de Lima-Neto
The Big Data era is affording a paradigm change on decision-making approaches. More and more, companies as well as individuals are relying on data rather than on the so called "gut feeling" to make decisions. However, searching the Web for carrying out purchases is not completely satisfactory yet, given the arduousness of finding suitable quality data. This has contributed to the emergence of data marketplaces as an alternative to traditional data commerce, as they provide appropriate online environments for data offering and purchasing. Nevertheless, as the number of available datasets to purchase increases, the task of buying appropriate offers is, very often, challenging. In this sense, we propose an intelligent decision support system to help buyers in purchasing data offers based on a multiple-criteria decision analysis. Experimental results show that our approach provides an interactive way that addresses buyers' needs, allowing them to state and easily refine their preferences, without any specific order, via a series of dataset recommendations.
{"title":"Intelligent decision support for data purchase","authors":"D. Martins, G. Vossen, Fernando Buarque de Lima-Neto","doi":"10.1145/3106426.3106434","DOIUrl":"https://doi.org/10.1145/3106426.3106434","url":null,"abstract":"The Big Data era is affording a paradigm change on decision-making approaches. More and more, companies as well as individuals are relying on data rather than on the so called \"gut feeling\" to make decisions. However, searching the Web for carrying out purchases is not completely satisfactory yet, given the arduousness of finding suitable quality data. This has contributed to the emergence of data marketplaces as an alternative to traditional data commerce, as they provide appropriate online environments for data offering and purchasing. Nevertheless, as the number of available datasets to purchase increases, the task of buying appropriate offers is, very often, challenging. In this sense, we propose an intelligent decision support system to help buyers in purchasing data offers based on a multiple-criteria decision analysis. Experimental results show that our approach provides an interactive way that addresses buyers' needs, allowing them to state and easily refine their preferences, without any specific order, via a series of dataset recommendations.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80130359","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}
Popular search engines have recently utilized the power of knowledge graphs (KGs) to provide specific answers to queries in a direct way. Search engine result pages (SERPs) are expected to provide facts in response to queries that satisfy semantic meaning. This encourages researchers to propose more influential knowledge graph generation techniques. To achieve and advance the technologies related to actionable knowledge graph presentation, creating action recommendations (ARs) is an essential step and a relatively new research direction to nurture research on generating KGs that are optimized for facilitating an entity's actions. An action represents the physical or mental activity of an entity. For example, for the entity "Donald J. Trump", typical potential actions could be "won the US presidential election" or "targets US journalists". In this paper, we describe the generation of relevant action recommendations based on entity instance and entity type. We propose two models that employ different approaches. Our first model exploits semisupervised learning and we introduce entity context vector (ECV) as an entity's distinguishing features for capturing the context of entities to reveal the similarity between entities, grounded on the prominent word2vec model. The second model is a probabilistic approach based on the Naive Bayes Theorem. We extensively evaluate our proposed models. Our first model significantly outperforms probabilistic and supervised learning-based models.
流行的搜索引擎最近利用知识图(KGs)的力量，以直接的方式为查询提供特定的答案。期望搜索引擎结果页(serp)为满足语义的查询提供事实响应。这鼓励研究人员提出更有影响力的知识图谱生成技术。为了实现和推进与可操作的知识图谱表示相关的技术，创建行动建议(ARs)是一个必要的步骤，也是一个相对较新的研究方向，以促进生成优化的知识图谱，以促进实体的行动。动作代表一个实体的身体或精神活动。例如，对于实体“Donald J. Trump”，典型的潜在行动可能是“赢得美国总统大选”或“针对美国记者”。在本文中，我们描述了基于实体实例和实体类型的相关操作建议的生成。我们提出了采用不同方法的两个模型。我们的第一个模型利用了半监督学习，我们引入了实体上下文向量(ECV)作为实体的区分特征，用于捕获实体的上下文，以揭示实体之间的相似性，以著名的word2vec模型为基础。第二个模型是基于朴素贝叶斯定理的概率方法。我们广泛地评估我们提出的模型。我们的第一个模型明显优于基于概率和监督学习的模型。
{"title":"Entity oriented action recommendations for actionable knowledge graph generation","authors":"Md. Mostafizur Rahman, A. Takasu","doi":"10.1145/3106426.3106546","DOIUrl":"https://doi.org/10.1145/3106426.3106546","url":null,"abstract":"Popular search engines have recently utilized the power of knowledge graphs (KGs) to provide specific answers to queries in a direct way. Search engine result pages (SERPs) are expected to provide facts in response to queries that satisfy semantic meaning. This encourages researchers to propose more influential knowledge graph generation techniques. To achieve and advance the technologies related to actionable knowledge graph presentation, creating action recommendations (ARs) is an essential step and a relatively new research direction to nurture research on generating KGs that are optimized for facilitating an entity's actions. An action represents the physical or mental activity of an entity. For example, for the entity \"Donald J. Trump\", typical potential actions could be \"won the US presidential election\" or \"targets US journalists\". In this paper, we describe the generation of relevant action recommendations based on entity instance and entity type. We propose two models that employ different approaches. Our first model exploits semisupervised learning and we introduce entity context vector (ECV) as an entity's distinguishing features for capturing the context of entities to reveal the similarity between entities, grounded on the prominent word2vec model. The second model is a probabilistic approach based on the Naive Bayes Theorem. We extensively evaluate our proposed models. Our first model significantly outperforms probabilistic and supervised learning-based models.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80982480","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}
Local community finding algorithms are helpful for finding communities around a seed node especially when the network is large and a global method is too slow. Most local methods find only a single community or are required to be run several times over different seed nodes to create multiple communities. In this paper, we present a new algorithm, LCHI that finds multiple, overlapping communities around a single node. Examples and analyses are presented support the effectiveness of LCHI.
{"title":"LCHI: multiple, overlapping local communities","authors":"Moeen Farasat, J. Scripps","doi":"10.1145/3106426.3106438","DOIUrl":"https://doi.org/10.1145/3106426.3106438","url":null,"abstract":"Local community finding algorithms are helpful for finding communities around a seed node especially when the network is large and a global method is too slow. Most local methods find only a single community or are required to be run several times over different seed nodes to create multiple communities. In this paper, we present a new algorithm, LCHI that finds multiple, overlapping communities around a single node. Examples and analyses are presented support the effectiveness of LCHI.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81726953","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}
Recommender System has been successfully applied to assist user's decision making by providing a list of recommended items. Context-aware recommender system additionally incorporates contexts (such as time and location) into the system to improve the recommendation performance. The development of context-aware recommender systems brings a new opportunity - context suggestion which refers to the task of recommending appropriate contexts to the users to improve user experience. In this paper, we explore the question whether user's contextual ratings can be reused to produce context suggestions. We propose two evaluation mechanisms for context suggestion, and empirically compare direct context predictions and indirect context suggestions based on a movie data that was collected from user studies. The experimental results reveal that indirect context suggestion works better than the direct context prediction, and tensor factorization is the best approach to produce context suggestions in our movie data.
{"title":"Context suggestion: empirical evaluations vs user studies","authors":"Yong Zheng","doi":"10.1145/3106426.3106466","DOIUrl":"https://doi.org/10.1145/3106426.3106466","url":null,"abstract":"Recommender System has been successfully applied to assist user's decision making by providing a list of recommended items. Context-aware recommender system additionally incorporates contexts (such as time and location) into the system to improve the recommendation performance. The development of context-aware recommender systems brings a new opportunity - context suggestion which refers to the task of recommending appropriate contexts to the users to improve user experience. In this paper, we explore the question whether user's contextual ratings can be reused to produce context suggestions. We propose two evaluation mechanisms for context suggestion, and empirically compare direct context predictions and indirect context suggestions based on a movie data that was collected from user studies. The experimental results reveal that indirect context suggestion works better than the direct context prediction, and tensor factorization is the best approach to produce context suggestions in our movie data.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"19-20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82718068","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 privacy policies notify users of a Website how their personal information is collected, processed and stored. Against the background of rising privacy concerns, privacy policies seem to represent an influential instrument for increasing customer trust and loyalty. However, in practice, consumers seem to actually read privacy policies only in rare cases, possibly reflecting the common assumption stating that policies are hard to comprehend. By designing and implementing an automated extraction and readability analysis toolset that embodies a diversity of established readability measures, we present the first large-scale study that provides current empirical evidence on the readability of nearly 50,000 privacy policies of popular English-speaking Websites. The results empirically confirm that on average, current privacy policies are still hard to read. Furthermore, this study presents new theoretical insights for readability research, in particular, to what extent practical readability measures are correlated. Specifically, it shows the redundancy of several well-established readability metrics such as SMOG, RIX, LIX, GFI, FKG, ARI, and FRES, thus easing future choice making processes and comparisons between readability studies, as well as calling for research towards a readability measures framework. Moreover, a more sophisticated privacy policy extractor and analyzer as well as a solid policy text corpus for further research are provided.
{"title":"Large-scale readability analysis of privacy policies","authors":"Benjamin Fabian, Tatiana Ermakova, Tino Lentz","doi":"10.1145/3106426.3106427","DOIUrl":"https://doi.org/10.1145/3106426.3106427","url":null,"abstract":"Online privacy policies notify users of a Website how their personal information is collected, processed and stored. Against the background of rising privacy concerns, privacy policies seem to represent an influential instrument for increasing customer trust and loyalty. However, in practice, consumers seem to actually read privacy policies only in rare cases, possibly reflecting the common assumption stating that policies are hard to comprehend. By designing and implementing an automated extraction and readability analysis toolset that embodies a diversity of established readability measures, we present the first large-scale study that provides current empirical evidence on the readability of nearly 50,000 privacy policies of popular English-speaking Websites. The results empirically confirm that on average, current privacy policies are still hard to read. Furthermore, this study presents new theoretical insights for readability research, in particular, to what extent practical readability measures are correlated. Specifically, it shows the redundancy of several well-established readability metrics such as SMOG, RIX, LIX, GFI, FKG, ARI, and FRES, thus easing future choice making processes and comparisons between readability studies, as well as calling for research towards a readability measures framework. Moreover, a more sophisticated privacy policy extractor and analyzer as well as a solid policy text corpus for further research are provided.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82807438","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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