2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)最新文献_第8页

Blockchain based access control systems: State of the art and challenges 基于区块链的访问控制系统:现状和挑战

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-08-22 DOI: 10.1145/3350546.3352561

Sara Rouhani, R. Deters

引用次数: 77

Reinforcement Learning for Personalized Dialogue Management 个性化对话管理的强化学习

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-08-01 DOI: 10.1145/3350546.3352501

F. Hengst, M. Hoogendoorn, F. V. Harmelen, Joost Bosman

{"title":"Reinforcement Learning for Personalized Dialogue Management","authors":"F. Hengst, M. Hoogendoorn, F. V. Harmelen, Joost Bosman","doi":"10.1145/3350546.3352501","DOIUrl":"https://doi.org/10.1145/3350546.3352501","url":null,"abstract":"Language systems have been of great interest to the research community and have recently reached the mass market through various assistant platforms on the web. Reinforcement Learning methods that optimize dialogue policies have seen successes in past years and have recently been extended into methods that personalizethe dialogue, e.g. take the personal context of users into account. These works, however, are limited to personalization to a single user with whom they require multiple interactions and do not generalize the usage of context across users. This work introduces a problem where a generalized usage of context is relevant and proposes two Reinforcement Learning (RL)-based approaches to this problem. The first approach uses a single learner and extends the traditional POMDP formulation of dialogue state with features that describe the user context. The second approach segments users by context and then employs a learner per context. We compare these approaches in a benchmark of existing non-RL and RL-based methods in three established and one novel application domain of financial product recommendation. We compare the influence of context and training experiences on performance and find that learning approaches generally outperform a handcrafted gold standard. CCS CONCEPTS • Computing methodologies → Reinforcement learning; Discourse, dialogue and pragmatics; • Human-centered computing → Natural language interfaces.","PeriodicalId":171168,"journal":{"name":"2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134441245","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

Recommendation Engine for Lower Interest Borrowing on Peer to Peer Lending (P2PL) Platform 点对点(P2PL)平台低利率借款推荐引擎

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-07-20 DOI: 10.1145/3350546.3352528

K. Ren, Avinash Malik

引用次数: 3

TACAM: Topic And Context Aware Argument Mining 主题和上下文感知论证挖掘

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-05-26 DOI: 10.1145/3350546.3352506

Michael Fromm, Evgeniy Faerman, T. Seidl

{"title":"TACAM: Topic And Context Aware Argument Mining","authors":"Michael Fromm, Evgeniy Faerman, T. Seidl","doi":"10.1145/3350546.3352506","DOIUrl":"https://doi.org/10.1145/3350546.3352506","url":null,"abstract":"In this work we address the problem of argument search. The purpose of argument search is the distillation of pro and contra arguments for requested topics from large text corpora. In previous works, the usual approach is to use a standard search engine to extract text parts which are relevant to the given topic and subsequently use an argument recognition algorithm to select arguments from them. The main challenge in the argument recognition task, which is also known as argument mining, is that often sentences containing arguments are structurally similar to purely informative sentences without any stance about the topic. In fact, they only differ semantically. Most approaches use topic or search term information only for the first search step and therefore assume that arguments can be classified independently of a topic. We argue that topic information is crucial for argument mining, since the topic defines the semantic context of an argument. Precisely, we propose different models for the classification of arguments, which take information about a topic of an argument into account. Moreover, to enrich the context of a topic and to let models understand the context of the potential argument better, we integrate information from different external sources such as Knowledge Graphs or pre-trained NLP models. Our evaluation shows that considering topic information, especially in connection with external information, provides a significant performance boost for the argument mining task.","PeriodicalId":171168,"journal":{"name":"2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125989204","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: 18

Deep Neural Architecture Search with Deep Graph Bayesian Optimization 基于深度图贝叶斯优化的深度神经结构搜索

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-05-14 DOI: 10.1145/3350546.3360740

Lizheng Ma, Jiaxu Cui, Bo Yang

{"title":"Deep Neural Architecture Search with Deep Graph Bayesian Optimization","authors":"Lizheng Ma, Jiaxu Cui, Bo Yang","doi":"10.1145/3350546.3360740","DOIUrl":"https://doi.org/10.1145/3350546.3360740","url":null,"abstract":"Image recognition aims to identify objects, places, people, or other targeted items in a given image, and has a wide range of social applications such as natural disasters recognition, plant disease detection, and traffic jam detection. Currently state-of-the-art methods of image recognition are based on deep learning and remain a common pattern in designing and using convolutional neural networks (CNNs). However, designing CNNs is extremely time intensive and requires an expert. Neural architecture search (NAS) can solve this problem by automatically identifying architectures of CNNs that are superior to hand-designed ones. Recently BO has been applied to neural architecture search and shows better performance than pure evolutionary strategies. All these methods adopt Gaussian processes (GPs) as surrogate function, with the handcraft similarity metrics as input. In this work, we propose a Bayesian graph neural network as a new surrogate, which can automatically extract features from deep neural architectures, and use such learned features to fit and characterize black-box objectives and their uncertainty. Based on the new surrogate, we then develop a graph Bayesian optimization framework to address the challenging task of deep neural architecture search. Experiment results show our method significantly outperforms the comparative methods on benchmark tasks. CCS CONCEPTS • Networks → Network performance evaluation; • Computing methodologies → Artificial intelligence.","PeriodicalId":171168,"journal":{"name":"2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"5 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127401969","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: 42

Check-It: A plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the Web Check-It:一个用于检测和减少网络上假新闻和错误信息传播的插件

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-05-10 DOI: 10.1145/3350546.3352534

Demetris Paschalides, Alexandros Kornilakis, Chrysovalantis Christodoulou, R. Andreou, G. Pallis, M. Dikaiakos, E. Markatos

引用次数: 24

GP-HD: Using Genetic Programming to Generate Dynamical Systems Models for Health Care GP-HD:使用遗传规划生成卫生保健动态系统模型

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-04-11 DOI: 10.1145/3350546.3352494

M. Hoogendoorn, W. V. Breda, Jeroen Ruwaard

引用次数: 0

Dialogue Based Recommender System that Flexibly Mixes Utterances and Recommendations 基于对话的推荐系统，灵活地混合了话语和推荐

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-04-08 DOI: 10.1145/3350546.3352500

Daisuke Tsumita, T. Takagi

引用次数: 11