Proceedings of the 4th ACM IKDD Conferences on Data Sciences最新文献

Multi-criteria Recommendations through Preference Learning 通过偏好学习的多标准推荐

Proceedings of the 4th ACM IKDD Conferences on Data Sciences Pub Date : 2017-03-09 DOI: 10.1145/3041823.3041824

R. Sreepada, Bidyut Kr. Patra, Antonio Hernando

{"title":"Multi-criteria Recommendations through Preference Learning","authors":"R. Sreepada, Bidyut Kr. Patra, Antonio Hernando","doi":"10.1145/3041823.3041824","DOIUrl":"https://doi.org/10.1145/3041823.3041824","url":null,"abstract":"In today's internet era, recommender system (RS) addresses information overload problem, which is common in many information driven domains. RS helps users chose a set of appropriate options from a plethora of options. Traditional single rating recommender systems have been playing a vital role over the decades in various domains. However, it is limited in a sense of providing user's accurate preferences about an item or services to the recommendation engine. The single rating recommender systems receive a single rating about an item, due to which these systems are inadequate to understand the reasons behind users' choice of items. On the other hand, multi-criteria rating systems allow the users to share more information about user's interest/ disinterest through multiple criteria of an item. Therefore, the multi-criteria recommender engine gets more information from the users and provides relevant recommendations to the users. In this paper, we propose a novel technique to learn and rank users' preferences over different criteria. Dominant criteria of each item are also learnt and ranked in the proposed technique. The obtained ranks are exploited to predict the overall rating by adapting the traditional user-based and item-based collaborative filtering techniques. We conducted experiments on two real world datasets (TripAdvisor and Yahoo! Movies) and our approach outperforms the traditional single rating systems and existing approaches on multi-criteria recommender systems.","PeriodicalId":173593,"journal":{"name":"Proceedings of the 4th ACM IKDD Conferences on Data Sciences","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127939145","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}

引用次数: 8

Hybrid Trust-Aware Model for Personalized Top-N Recommendation 个性化Top-N推荐的混合信任感知模型

Proceedings of the 4th ACM IKDD Conferences on Data Sciences Pub Date : 2017-03-09 DOI: 10.1145/3041823.3041829

Arpit Merchant, Navjyoti Singh

引用次数: 4

Differentiating Code-Borrowing from Code-Mixing 区分代码借用与代码混合

Proceedings of the 4th ACM IKDD Conferences on Data Sciences Pub Date : 2017-03-09 DOI: 10.1145/3041823.3067692

Neha Prabhugaonkar, Sai Kiran Peketi, Kavita Ganeshan, U. Sureshkumar

引用次数: 0

Supervised Analysis Dictionary Learning: Application in Consumer Electronics Appliance Classification 监督分析字典学习:在消费电子产品分类中的应用

Proceedings of the 4th ACM IKDD Conferences on Data Sciences Pub Date : 2017-03-09 DOI: 10.1145/3041823.3041825

Protim Bhattacharjee, Shisagnee Banerjee, Manoj Gulati, A. Majumdar, S. S. Ram

引用次数: 2

Modeling and detecting change in user behavior through his social media posting using cluster analysis 通过使用聚类分析，通过他的社交媒体帖子建模和检测用户行为的变化

Proceedings of the 4th ACM IKDD Conferences on Data Sciences Pub Date : 2017-03-09 DOI: 10.1145/3041823.3041830

Deepali J. Joshi, Nikhil Supekar, R. Chauhan, Manasi S. Patwardhan

{"title":"Modeling and detecting change in user behavior through his social media posting using cluster analysis","authors":"Deepali J. Joshi, Nikhil Supekar, R. Chauhan, Manasi S. Patwardhan","doi":"10.1145/3041823.3041830","DOIUrl":"https://doi.org/10.1145/3041823.3041830","url":null,"abstract":"According to World Health Organization, one of the greatest health hazards of 21st century is mental disorder. Unlike any physical illness, mental illness is not that apparent to be recognized at early stages. Also, especially in India, patients do not come forward to seek help because of the social taboo or inferiority that is associated with these diseases. As per World Health Organization, 11 percent of the world's population suffer from mental disorders but only 1 percent of the population form the community of experts who can treat them, leading to the lack of sufficient man power to treat mental illness, and thus the treatments being very expensive. This calls for a strong need for a technique to automatically identify non-normal behavior of a person, which would serve as an indicator for early detection of mental illness. According to the census report of India 2011, the citizens from the age group of 18 to 30 is the majority having mental health problems, which is incidentally the age group which is very active on social networking sites. Online social networks serve as a valuable source of information about people through their published interests, attributes and social interactions and also a true mirror of their behavior. People who don't share their issues with friends and families then find a place on social media and open up their feelings there. Majority of work in current relevant literature talks about classifying tweets based on the sentiments. Whereas, our approach is to analy se the tweets of a person over a period of time to track the change in his behaviour if any. We have developed a new unsupervised technique for detecting change in behaviour of a person using the difference in the structural and behavioural feature vector and defining a threshold using iterative clustering. With a synthesized data, our models lead us to 92% accuracy and a precision 92 % and recall of 90 %.","PeriodicalId":173593,"journal":{"name":"Proceedings of the 4th ACM IKDD Conferences on Data Sciences","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117324716","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}

引用次数: 10

Borrowing Likeliness Ranking based on Relevance Factor 基于关联因子的借款可能性排名

Proceedings of the 4th ACM IKDD Conferences on Data Sciences Pub Date : 2017-03-09 DOI: 10.1145/3041823.3067694

R. Rajalakshmi, Rohan Agrawal

引用次数: 9

A user activity-based measurement study characterizing and classifying Stack Exchange communities across multiple domains 基于用户活动的度量研究，对跨多个域的Stack Exchange社区进行表征和分类

Proceedings of the 4th ACM IKDD Conferences on Data Sciences Pub Date : 2017-03-09 DOI: 10.1145/3041823.3041834

Akshit Trehan, S. Khurana, A. Bagchi

{"title":"A user activity-based measurement study characterizing and classifying Stack Exchange communities across multiple domains","authors":"Akshit Trehan, S. Khurana, A. Bagchi","doi":"10.1145/3041823.3041834","DOIUrl":"https://doi.org/10.1145/3041823.3041834","url":null,"abstract":"In recent times, Question-Answer communities have engaged much user attention and have become a major platform for knowledge sharing and discussion. Stack Exchange (SE) is one such successful community which is a collection of various domain-specific forums, each acting as an independent community in itself. In this paper, we undertake a comparative measurement study across a large number of these domain-specific forums within Stack Exchange. We analyse a number of user activity-based features of each forum and try to cluster different forums based on their similarities on this feature set. For our study, we model Stack Exchange as an Across \"Forum Graph\" based on inter-forum similarity, and its individual forums as: (a) A user-to-user graph (question asker-answerer) (b) A bipartite graph between questions and answerers, and (c) A bipartite graph between questions and answers. Through these graphs we present a measurement study of Stack Exchange which focuses on the similarities and differences between various forums based on the patterns of user activity on them. The clusters obtained give a high level idea of similar forums based on common users and content. We observe that communities can be classified as \"discussion-based\" and \"fact-based\" and further we classify forums on the basis of question answering patterns.","PeriodicalId":173593,"journal":{"name":"Proceedings of the 4th ACM IKDD Conferences on Data Sciences","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121680407","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}

引用次数: 0

Spatio-temporal Autocorrelation Analysis for Regional Land-cover Change Detection from Remote Sensing Data 基于遥感数据的区域土地覆盖变化时空自相关分析

Proceedings of the 4th ACM IKDD Conferences on Data Sciences Pub Date : 2017-03-09 DOI: 10.1145/3041823.3041835

Monidipa Das, S. Ghosh

{"title":"Spatio-temporal Autocorrelation Analysis for Regional Land-cover Change Detection from Remote Sensing Data","authors":"Monidipa Das, S. Ghosh","doi":"10.1145/3041823.3041835","DOIUrl":"https://doi.org/10.1145/3041823.3041835","url":null,"abstract":"Of the various applications of remote sensing data, characterizing the land-cover dynamics is of utmost significance, providing insights into science, management policy, and several regulatory actions. Recent research works indicate that there is a need to understand and monitor land-cover dynamics at regional scale rather than local scale. However, the regional change is a more generalized concept and therefore, the use of pixel based analysis alone may not be sufficient to get proper insights regarding the land-cover change in remotely sensed imagery. Moreover, higher spectral variation and mixed pixels are two key challenges imposed by satellite imagery, resulting into poor performance of existing pixel-based methods for regional land-cover change detection. In this work, we have proposed a novel approach for detecting regional land-cover changes in satellite imagery using spatio-temporal autocorrelation analysis. Autocorrelation among the neighborhood pixels at various spatio-temporal lags has been utilized here to address the problem of mixed pixel and spectral variation. An index (γ), based on the estimated autocorrelations, has been proposed to classify the regions as 'change' and 'no-change' regions. Moreover, a parameter (σ) has been introduced to provide the measure of regional change significance. The method has been evaluated with Landsat ETM+ imagery (30m resolution) of four zones in and around Kolkata (India), comprising a total of 430 sq. km area (ã 4.8 × 105 pixels). The experimental results are encouraging, with an overall accuracy of 90.66%.","PeriodicalId":173593,"journal":{"name":"Proceedings of the 4th ACM IKDD Conferences on Data Sciences","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126328277","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}

引用次数: 6

Recommending resolutions of ITIL services tickets using Deep Neural Network 使用深度神经网络推荐ITIL服务票据的解决方案

Proceedings of the 4th ACM IKDD Conferences on Data Sciences Pub Date : 2017-03-09 DOI: 10.1145/3041823.3041831

Durga Prasad Muni, Suman Roy, Y. Chiang, Antoine Jean-Marie Viallet, Navin Budhiraja

{"title":"Recommending resolutions of ITIL services tickets using Deep Neural Network","authors":"Durga Prasad Muni, Suman Roy, Y. Chiang, Antoine Jean-Marie Viallet, Navin Budhiraja","doi":"10.1145/3041823.3041831","DOIUrl":"https://doi.org/10.1145/3041823.3041831","url":null,"abstract":"Application development and maintenance is a good example of Information Technology Infrastructure Library (ITIL) services in which a sizable volume of tickets are raised everyday for different issues to be resolved in order to deliver uninterrupted service. An issue is captured as summary on the ticket and once a ticket is resolved, the solution is also noted down on the ticket as resolution. It will be beneficial to automatically extract information from the description of tickets to improve operations like identifying critical and frequent issues, grouping of tickets based on textual content, suggesting remedial measures for them etc. In particular, the maintenance people can save a lot of effort and time if they have access to past remedial actions for similar kind of tickets raised earlier based on history data. In this work we propose an automated method based on deep neural networks for recommending resolutions for incoming tickets. We use ideas from deep structured semantic models (DSSM) for web search for such resolution recovery. We project a small subset of existing tickets in pairs and an incoming ticket to a low dimensional feature space, following which we compute the similarity of an existing ticket with the new ticket. We select the pair of tickets which has the maximum similarity with the incoming ticket and publish both of its resolutions as the suggested resolutions for the latter ticket. The experiment of our data sets shows that we are able to achieve a promising similarity match of about 70% - 90% between the suggestions and the actual resolution.","PeriodicalId":173593,"journal":{"name":"Proceedings of the 4th ACM IKDD Conferences on Data Sciences","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133113468","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}

引用次数: 7

Embedding Learning of Figurative Phrases for Emotion Classification in Micro-Blog Texts 微博文本情感分类中比喻短语的嵌入学习

Proceedings of the 4th ACM IKDD Conferences on Data Sciences Pub Date : 2017-03-09 DOI: 10.1145/3041823.3041828

Shreshtha Mundra, Sandya Mannarswamy, Manjira Sinha, Anirban Sen

{"title":"Embedding Learning of Figurative Phrases for Emotion Classification in Micro-Blog Texts","authors":"Shreshtha Mundra, Sandya Mannarswamy, Manjira Sinha, Anirban Sen","doi":"10.1145/3041823.3041828","DOIUrl":"https://doi.org/10.1145/3041823.3041828","url":null,"abstract":"Figurative phrases such as idioms are a type of Multi-Word Expressions (MWE) that possess a specialized meaning, which is independent and different from the literal meaning of the constituent words. Figurative language is widely used to express emotions and are very predominant in micro-blog data.Therefore, an efficient model of emotion categorization for micro-blogs should be able to correctly represent the instances of figurative phrases in the data. However, due to their non-compositional nature, the phrasal representation of figurative language cannot be directly obtained from the constituent words and hence this requires novel approaches for addressing the problem of modeling figurative phrases in micro-blogs. Most of the existing methods of modeling figurative idiomatic phrases in traditional text data use the broader textual context available for better results. However, in case of micro-blog data, such large context is not available due to very short length of text, which poses an additional challenge. Given the need to model figurative language for emotion classification, this paper develops the novel idea of Emotion Sensitive Figurative Phrase Embedding (ESFPE) to model idiomatic phrases in micro-blog data and show upto 14% improvement in emotion classification performance over baseline. To the best of our knowledge, this is the first work towards figurative phrase modeling for emotion classification in micro-blog text.","PeriodicalId":173593,"journal":{"name":"Proceedings of the 4th ACM IKDD Conferences on Data Sciences","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128020767","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}

引用次数: 2