2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)最新文献_第8页

A Contextual Random Walk Model for Automated Playlist Generation 自动生成播放列表的上下文随机游走模型

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.00-66

Seiji Ueda, Atsushi Keyaki, Jun Miyazaki

{"title":"A Contextual Random Walk Model for Automated Playlist Generation","authors":"Seiji Ueda, Atsushi Keyaki, Jun Miyazaki","doi":"10.1109/WI.2018.00-66","DOIUrl":"https://doi.org/10.1109/WI.2018.00-66","url":null,"abstract":"In this paper, we propose new methods for generating playlists with a single graph, which represents multiple types of relations in a playlist. Although current users are familiar with online music services, they have difficulty in deciding which tracks to listen to because there are millions of tracks available on such services. Automated playlist generation is one of the best solutions to solving this costly task of finding interesting tracks from the enormous tracks. Accordingly, one playlist-generation task, namely, hit rate, in which several tracks are given as a user query, is focused on in this study. There are four types of context objects (playlists, tracks, artists, and users) in the basic information on playlists, and three types of relations (playlists contain tracks and artists, users create playlists and artists play and/or sing tracks) in playlists. First, different types of relations in playlists are combined, and a single graph linking different context objects is generated. Next, a random walk is applied to the graph, and the expected values of track nodes are calculated on the basis of the transition probabilities of nodes in the graph. Finally, tracks are recommended in order of the expected values. The results of an experimental evaluation of the proposed methods in comparison with conventional methods revealed that one of the proposed methods (RW-hybrid) improved effectiveness by up to 21%. Moreover, this method reduces execution time as much as the fastest existing methods.","PeriodicalId":405966,"journal":{"name":"2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128199825","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: 3

NavWalker: Information Augmented Network Embedding NavWalker:信息增强网络嵌入

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.0-113

Kwei-Herng Lai, Chih-Ming Chen, Ming-Feng Tsai, Chuan-Ju Wang

引用次数: 3

[Copyright notice] (版权)

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/wi.2018.00003

引用次数: 0

Social CRM from the Customer Perspective: A Preliminary Analysis of Differences between Brazilian and German Users 客户视角下的社交CRM:巴西和德国用户差异的初步分析

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.000-1

Julio A. N. Viana, Richard Stüber, Olaf Reinhold, R. Alt

引用次数: 0

Image Sentiment Analysis Using Deep Learning 使用深度学习的图像情感分析

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.00-11

Namita Mittal, Divya Sharma, M. Joshi

{"title":"Image Sentiment Analysis Using Deep Learning","authors":"Namita Mittal, Divya Sharma, M. Joshi","doi":"10.1109/WI.2018.00-11","DOIUrl":"https://doi.org/10.1109/WI.2018.00-11","url":null,"abstract":"Sentiments are feelings, emotions likes and dislikes or opinions which can be articulate through text, images or videos. Sentiment Analysis on web data is now becoming a budding research area of social analytics. Users express their sentiments on the web by exchanging texts and uploading images through a variety of social media like Instagram, Facebook, Twitter, WhatsApp etc. A lot of research work has been done for sentiment analysis of textual data; there has been limited work that focuses on analyzing the sentiment of image data. Image sentiment concepts are ANPs i.e. Adjective Noun Pairs automatically discovered tags of web images which are useful for detecting the emotions or sentiments conveyed by the image. The major challenge is to predict or identify the sentiments of unlabelled images. To overcome this challenge deep learning techniques are used for sentiment analysis, as deep learning models have the capability for effectively learning the image behavior or polarity. Image recognition, image prediction, image sentiment analysis, and image classification are some of the fields where Neural Network (NN) has performed well implying significant performance of deep learning in image sentiment analysis. This paper focuses on some of the noteworthy models of deep learning as Deep Neural Network (DNN), Convolutional Neural Network (CNN), Region-based CNN (R-CNN) and Fast R-CNN along with the suitability of their applications in image sentiment analysis and their limitations. The study also discusses the challenges and perspectives of this rising field.","PeriodicalId":405966,"journal":{"name":"2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115184496","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: 30

Fine-Grained Deep Knowledge-Aware Network for News Recommendation with Self-Attention 面向自关注新闻推荐的细粒度深度知识感知网络

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.0-104

Jie Gao, Xin Xin, Junshuai Liu, Rui Wang, Jing Lu, Biao Li, Xin Fan, Ping Guo

{"title":"Fine-Grained Deep Knowledge-Aware Network for News Recommendation with Self-Attention","authors":"Jie Gao, Xin Xin, Junshuai Liu, Rui Wang, Jing Lu, Biao Li, Xin Fan, Ping Guo","doi":"10.1109/WI.2018.0-104","DOIUrl":"https://doi.org/10.1109/WI.2018.0-104","url":null,"abstract":"On-line news reading has become the most popular way for user to obtain real-time information. With the millions of news, it is a key challenge to help user find the articles that are interesting to read. Although great achievements have been made, there is little work to focus on combing news language with external knowledge graphs and expanding news text from a word-level. Taking this issue into consideration, we introduce a novel self-attention based mechanism in news recommendation. The key component of our model is multiple self-attention modules: the word-level attention, which takes tags of news, entities in external knowledge graph and entities' contexts as the input to calculate the semantic-level and knowledge-level representation of the news; the item-level attention module, which used to fuse the two-level representation into the same low-dimension and get a overall embedding of user history behavior sequence. Specially, in order to deal with the diversity of user preferences, we use another self-attention module dynamically aggregate user click history and select candidate news. And finally, a multi-head attention module is used to connect history and candidate news and then calculate the click-through-rate(CTR) via a fully connected layer. Through amount of experiments on a real-world online news website, we demonstrate that our model outperforms better results than previous start-of-art recommendation models.","PeriodicalId":405966,"journal":{"name":"2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121244642","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: 14

DOMdiff: Identification and Classification of Inter-DOM Modifications DOMdiff: dom间修改的识别和分类

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.00-81

Manuel Leithner, D. Simos

{"title":"DOMdiff: Identification and Classification of Inter-DOM Modifications","authors":"Manuel Leithner, D. Simos","doi":"10.1109/WI.2018.00-81","DOIUrl":"https://doi.org/10.1109/WI.2018.00-81","url":null,"abstract":"Current web crawlers, document databases and change monitoring systems for web sites are commonly limited to static content and analysis of code as retrieved from the server, an approach that is not suitable for modern dynamic web applications. The canonical representation of the contents of a single web page at any given time is an instance of the Document Object Model (DOM), a tree structure that forms the basis for rendering and processing of the page within the browser and is updated when content is modified. This work presents DOMdiff, an algorithm to identify changes between two different DOM instances, as well as a method to classify these changes in terms of a ranking that represents the distance between the two trees. We compare a manually derived classifier with the results of PRank, a ranked version of the Perceptron algorithm, a simple machine learning approach that generates a multiclass classifier based on formulae in a constrained predicate logic, and the established statistical classifier C5.0. Our results indicate that DOMdiff is suitable to large-scale change identification and that entropy-based statistical classifiers are more accurate than our simple predicate-based classifier for the problem at hand, but require a larger decision tree. We additionally identify a shortcoming of PRank when handling features with low information gain/high entropy.","PeriodicalId":405966,"journal":{"name":"2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116440737","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

Reputation Management in Multi-Agent Systems Using Permissioned Blockchain Technology 使用许可区块链技术的多代理系统中的声誉管理

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.000-5

D. Calvaresi, V. Mattioli, Alevtina Dubovitskaya, A. Dragoni, M. Schumacher

{"title":"Reputation Management in Multi-Agent Systems Using Permissioned Blockchain Technology","authors":"D. Calvaresi, V. Mattioli, Alevtina Dubovitskaya, A. Dragoni, M. Schumacher","doi":"10.1109/WI.2018.000-5","DOIUrl":"https://doi.org/10.1109/WI.2018.000-5","url":null,"abstract":"The multi-agent framework is a well-known approach to realize distributed intelligent systems. Multi-agent systems (MAS) are increasingly employed in safety-and information-critical domains (e.g., eHealth, cyber-physical systems, financial services, and energy market). Therefore, these systems need to be equipped with mechanisms to ensure transparency and the trustworthiness of the behaviors of their components. Trust can be achieved by employing reputation-based mechanisms. Nevertheless, the existing methods are still unable to fully guarantee the desired accountability and transparency. Aligned with the recent trends, advocating the distribution of trust to avoid the risks of having a single point of failure of the system, this work extends existing efforts on combining blockchain technologies (BCT) and MAS. To attain a trusted environment, we provide the architecture and implementation of a system that allows the agents to interact with each other and enables tracking how their reputation changes after every interaction. Agents reputations are computed transparently using smart contracts. Immutable distributed ledger stores reputation values, as well as services and their evaluations to ensure trustworthy interactions between the agents. We also developed a graphical interface to test different scenarios of interactions between the agents. Finally, we summarize and discuss the experience gained and explain the strategic choices when binding MAS and BCT.","PeriodicalId":405966,"journal":{"name":"2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122370763","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: 21

High-Speed Clustering of Regional Photos Using Representative Photos of Different Regions 基于不同区域代表性照片的区域照片高速聚类

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.00-43

Takayasu Fushimi, Ryota Mori

{"title":"High-Speed Clustering of Regional Photos Using Representative Photos of Different Regions","authors":"Takayasu Fushimi, Ryota Mori","doi":"10.1109/WI.2018.00-43","DOIUrl":"https://doi.org/10.1109/WI.2018.00-43","url":null,"abstract":"In recent years, a huge number of photographs have been posted on SNS by many users, and users view photos posted by other users. When browsing photos, even if you find a photo of the scenery you want to see, it is difficult to go to that place if you were taken at a remote location such as overseas. Then, there are demands to search for areas that look like the photo in nearby places. To this end, there is a method of extracting representative photos for each area and clustering a large number of photos based on the representative ones. The k-medoids clustering method extracts representative objects called medoids and clusters them, so it coincides with this purpose, but it takes a large amount of computation time. In this paper, we aim to propose two methods of speeding up for k-medoids clustering utilizing representative photos in other areas which have been already extracted. In a method using representative photos of a single area, the clustering quality varies depending on the area to be used. It is difficult to know in advance the area that increases the clustering quality. In a method of selecting from representative photos in multiple regions, it is expected that highly accurate clustering results can be obtained because the representative photographs that minimize the objective function of the k-medoids method are selected across regions. In our experimental evaluation using large real datasets, we confirm that our proposed method works much faster than existing methods, greedy methods equipped with the lazy evaluation and the pivot pruning techniques, and obtains high quality.","PeriodicalId":405966,"journal":{"name":"2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126206317","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

Context Prediction in the Social Web Using Applied Machine Learning: A Study of Canadian Tweeters 使用应用机器学习的社交网络语境预测:对加拿大推特用户的研究

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.00-85

Hamman W. Samuel, Benyamin Noori, Sara Farazi, Osmar R Zaiane

{"title":"Context Prediction in the Social Web Using Applied Machine Learning: A Study of Canadian Tweeters","authors":"Hamman W. Samuel, Benyamin Noori, Sara Farazi, Osmar R Zaiane","doi":"10.1109/WI.2018.00-85","DOIUrl":"https://doi.org/10.1109/WI.2018.00-85","url":null,"abstract":"In this ongoing work, we present the Grebe social data aggregation framework for extracting geo-fenced Twitter data for analysis of user engagement in health and wellness topics. Grebe also provides various visualization tools for analyzing temporal and geographical health trends. Grebe currently has over 18 million indexed public tweets, and is the first of its kind for Canadian researchers. The large dataset is used for analyzing three types of contexts: geographical context via prediction of user location using supervised learning, topical context via determining health-related tweets using various learning approaches, and affective context via sentiment analysis of tweets using rule-based methods. For the first, we define user location as the position from which users are posting a tweet and use standard precision metrics for evaluation with promising results for predicting provinces and cities from tweet text. For the second, we use a broader definition of health using the six dimensions of wellness model and evaluate using manually annotated documents with good results using supervised and semi-supervised machine learning. For the third, we use the indexed tweets to show current trends in emotions and opinions and demonstrate trends in polarity and emotions across various Canadian provinces. The combination of these contexts provides useful insights for digital epidemiology. Ultimately, the vision of Grebe is to provide researchers with Canada-specific social web datasets through an open source platform with an accessible RESTful API, and this paper showcases Grebe's potential and presents our progress towards achieving these goals.","PeriodicalId":405966,"journal":{"name":"2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133595674","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: 5